From external to internal: The influence of ousted corrupt officials on CEO pay-for-performance sensitivity

From external to internal: The influence of ousted corrupt officials on CEO pay-for-performance sensitivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From external to internal: The influence of ousted corrupt officials on CEO pay-for-performance sensitivity Hongfei Ruan, Yi Xiang, Ying Zhang, Li Tong, Yongzhi Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7477999/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract We leverage an exogenous shock—the ousted corrupt officials in anticorruption campaign—and examine how the CEO pay-for-performance sensitivity (PPS) was affected. We argue that the staggered removal of provincial officials on corruption charges during China’s anticorruption campaign leads to increased firm exposure to anticorruption enforcement, prompting firms to transition from external to internal attribution. We suggest that the firm exposure to anticorruption enforcement (ousted corrupt officials in firm’s location) will increase CEO PPS in a firm. Furthermore, we examine how the effect of firm exposure to anticorruption enforcement on CEO PPS varies across different levels of political involvement. Our analysis reveals that the positive association between firm exposure to anticorruption enforcement and CEO PPS is attenuated for state-owned enterprises (SOEs). Conversely, this relationship is amplified for firms with greater reliance on entertainment expenses or when the political embeddedness of ousted corrupt officials is more pronounced. This study makes significant contributions to the literature on CEO PPS and business-government connections. Business and commerce/Business and management Social science/Business and management Earth and environmental sciences/Environmental social sciences CEO pay-for-performance sensitivity ousted corrupt officials firm exposure to anticorruption enforcement internal attribution anticorruption Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION A primary role of board involves in determining CEO pay (Core, Holthausen, & Larcker, 1999 ; Jensen & Murphy, 1990 ; Laux & Laux, 2009 ; Seo, 2017 ). To decide CEO PPS is regarded as one of the most important considerations since it’s a pivotal component of CEO compensation design and reflects a critical mechanism for aligning the interests of executives and shareholders (Bebchuk & Fried, 2003 ; Chadwick, Guthrie, Xing, & Yan, 2024 ; Garen, 1994 ; Van Essen, Otten, & Carberry, 2015 ). The majority of studies to date almost exclusively focused on how internal agency-related factors shapes CEO PPS, such as level of strategic investments (Shi, Connelly, Mackey, & Gupta, 2019 ), CEO political promotion (Cao, Lemmon, Pan, Qian, & Tian, 2019 ; Perry & Zenner, 2001 ), and firm family-controlled status (Chen, Chittoor, & Vissa, 2021 ). However, we know little about how external sociopolitical factors influence a firm’s policy of CEO PPS. Studying the impact of sociopolitical factors on CEO PPS is important because "institutional pressures, ..., do not just "enter" an organization—they are interpreted, given meaning and "represented" by occupants of structural positions" (Greenwood, Raynard, Kodeih, Micelotta, & Lounsbury, 2011 : 342). To advance the research, our study leverages the context of anticorruption campaign, the landmark sociopolitical event in China (Ben, Li, Duncan, & Xu, 2020 ; Cao, Wang, & Zhou, 2018 ; Pan & Tian, 2020 ). This campaign results in significant changes in institutional environment and business practices. It led to a fundamental shift in the political landscape of China, impacting governance, power dynamics, and bureaucratic practices. The sheer number of officials investigated and disciplined underscores the campaign's extensive reach and its potential to reshape political behavior and norms (Fang et al., 2023 ; Griffin, Liu, & Shu, 2022 ). This campaign also influences business practices and corporate governance in China. The crackdown on corruption has altered the risk landscape for businesses, necessitating new strategies for compliance and corporate ethics (Griffin, Liu, & Shu, 2022 ; Hauser, 2019 ; Sari, Cahaya, & Joseph, 2021 ). We build on political perspective (Spicer, 2013 ) and attribution theory (Heider, 1958 ; Weiner, 1985 ) to theorize that business logics under politically corrupted environment are characterized with resource acquisition based on guanxi and nepotism (Fan, 2002 ; Huang & Rice, 2012 ). Anticorruption campaign will lead to a thorough revision of the regulatory framework governing market activities and give rises to a predictable and transparent regulatory environment as well as increasing marketization (Bu, Hanspal, & Liao, 2022 ; Cao, Wang, & Zhou, 2018 ). We thus suggest that, in a corrupted environment, firms will tend to make external attribution about corporate failure or success, i.e. , to attribute success or failure to government guanxi . However, after ousted corrupt officials in a firm’s location, firms will be exposed to anticorruption enforcement. We suggest that firms are more likely to make internal attribution, i.e., attribute success or failure to their own business acumen. In this regard, firms will hold CEOs become more responsible for financial performance, and the manifestation of such internal attribution of responsibility is the increase of CEO PPS. Thus, we posit that the firm exposure to anticorruption enforcement will increase a firm's CEO PPS. We further explore how the influence of firm exposure to anticorruption enforcement on CEO PPS varies across different types of political involvement. Our analyses show that the positive relationship between firm exposure to anticorruption enforcement and CEO PPS will be weaken if a firm is a SOEs. We also find that, when the political embeddedness of ousted corrupt officials due to anticorruption campaigns is larger, the positive relationship between firm exposure to anticorruption enforcement and CEO PPS will be strengthened. We also suggest that, when a firm has a stronger reliance on entertainment and travel costs (ETC), it will be more responsive to the anticorruption campaign, and the positive effects of firm exposure to anticorruption enforcement on CEO PPS will be strengthened. This study makes several contributions to extant literature. First, in contrast to the prevalent principal-agent viewpoint that traditionally elucidates corporate practice of CEO PPS (e.g., Chen, Chittoor, Vissa, 2021 ; Shi et al, 2019 ), we introduce a crucial yet understudied theoretical angle derived from the significant and pivotal change in political institutions in China to unravel how firms respond to the Chinese anticorruption campaign by altering the CEO’s PPS, thereby enriching the existing literature on the antecedents of CEO compensation design. Second, our contribution extends to the public governance literature by broadening the impact of significant form of public governance (e.g., ousted corrupt officials) from the previous focus on macroeconomics, financial markets, and corporate strategies (e.g., Chen, Lu, Heng, &Tan, 2023 ; Fang et al., 2023 ) to an efficient incentive mechanism in corporate governance (e.g., CEO PPS). Third, our study advances the business-government connections literature by theorizing how the distinctive contextual roles played by both sides of business-government connections—firms and officials—affect the relationship between firm exposure to anticorruption enforcement and CEO PPS. On one hand, ascribed business-government connections (e.g., SOEs) attenuate the aforementioned relationship, while achieved business-government connections (e.g., firms with higher ETC) strengthen it. On the other hand, ousted corrupt officials with larger local embeddedness amplify the strength of the above relationship. INSTITUTIONAL BACKGROUND AND HYPOTHESES CEO PPS Based on the agency theory, there is a conflict of interest between the owners (principals) and the managers (agents), and the agents may attempt to maximize their interests at the expense of the principals. One proposed solution to the agency problem is to align CEO compensation based on the firm performance (Brick, Palmon, & Wald, 2006 ; Murphy, 1985 ; Hoi, Wu, & Zhang, 2019 ). For instance, a body of research has identified that boards will reward CEOs for good performance and punish them for poor performance (Boschen, Duru, Gordon, & Smith, 2003 ). In fact, external stakeholders such as shareholders and analysts often criticize boards when CEO pay becomes separated from firm performance (Bebchuk & Fried, 2006 ). The CEO PPS is typically applied in the following scenarios. First, from the perspective of CEO characteristics, as an internal corporate governance mechanism, CEO compensation is one of the important ways to overcome the CEO’s risk aversion (Dittmann, Maug, & Spalt, 2010 ; Wruck & Wu, 2022 ). For instance, since Republican-leaning CEOs tend to be more conservative (Arikan, Kara, Masli, & Xi, 2023 ; Unsal, Hassan, & Zirek, 2016 ), boards thus offer more performance-based pay to these individuals. Second, when a company is in crisis or undergoing significant changes, CEOs may be granted higher compensation to facilitate major strategic transformations. Furthermore, the attitudes of board members play a crucial role in determining whether CEO pay for performance mechanisms are adopted, and this is closely tied to the decisions made by the CEO. For instance, when CEOs invest in high levels of growth, it provides the board with a target for their internal attributions, intensifying the PPS (Shi, Connelly, Mackey, & Gupta, 2019 ). Anticorruption Campaign in China In emerging markets such as China, the government controls and allocates resources that are critical to the survival and development of firms (Fengyan, Hongjuan, Tan & Qi, 2022 ). For example, rent-seeking government officials extract resources from firms through the imposition of concealed costs (Jung & Lee, 2023 ). Meanwhile, government possess the authority to either bestow preferential treatment upon businesses or levy additional fees and fines upon them, which cause government officials' rent-seeking incentive (Chen, Li, Su, & Sun, 2011 ; Gao, 2011 : 176). To address the increasing corruption, China has initiated an unparalleled, for-reaching, extensive, and lasting anticorruption campaign President Xi Jinping since the 18th National Congress of the Communist Party (Branigan, 2015 ; Giannetti, Liao, & Yu, 2021). Shortly after assuming leadership in China on November 15, 2012, Xi promptly promulgated the Eight-Point Regulation within the initial month of his tenure, manifesting a resolute commitment to combat corruption by addressing both high-ranking officials ("tigers") and lower-level functionaries ("flies") (Griffin, Liu & Shu, 2022 ). Over the preceding decade, the Central Commission for Discipline Inspection (CCDI), tasked with spearheading the anticorruption campaign and conducting inquiries into officials, has undertaken investigations involving nearly 5 million officials and party members across various echelons. With the notable successes in the anticorruption endeavors, the potential ramifications of these efforts on China's sociopolitical environment have garnered escalating scrutiny, and scholars have undertaken a substantial volume of research to examine the implications of anticorruption on the economy and financial markets (Griffin et al., 2022 ; Pan & Tian, 2020 ). For its impact on a firm's value, some studies found that the campaign decreases the value of political connections with private firms (e.g., Fengyan et al, 2021; Liu & Ying, 2019 ), but helps improve firm performance. In addition, it can influence firms' market strategies such as declining investment expenditure (Pan & Tian, 2020 ), and firms' non-market strategies such as reducing entertainment expenditures (Griffin, Liu & Shu, 2022 ), suppressing negative information release (Cao, Wang & Zhou, 2018 ). Exposure to Anticorruption Enforcement and PPS In China, the government controls vital resources key to firm survival and growth (Li, Meng, Wang, & Zhou, 2008 ; Marquis & Qian, 2014 ). Firms’ interaction with the government hinge more on external factors like political connections and bribery-based exchanges to secure business advantages and regulatory benefits (Cull, Li, Sun, & Xu, 2015 ; Millington et al., 2005 ). In environments marked by corruption, business operations predominantly rely on external attributions, where corporate managers often lack the autonomy to make independent decisions. The famous assertion by Lord Acton that 'power tends to corrupt, and absolute power corrupts absolutely,' underscores the overwhelming impact of corruption, which often restricts managerial discretion within the business environment. Firms are compelled to devote substantial resources to sustaining business-government relations, positioning these connections as central to business strategy rather than market-driven considerations (Iriyama, Kishore, & Talukdar, 2016 ). Therefore, business decisions often depend on external approvals and interactions, especially with government officials (e.g., Huang & Rice, 2012 ). It results in that the boards believe the key to business success is to win access to resources controlled by the government. This leads to an external attribution for business success and failure. However, after the ousted corrupt officials, firms are less subjective to government intervention and less depend on the resources controlled by government (Fang et al., 2023 ; Griffin et al., 2022 ; Jiang et al., 2021 ). This shift generally leads to a reduced emphasis on leveraging government resources and an increased focus on enhancing internal management practices and fostering innovation (Anokhin & Schulze, 2009 ; Iriyama, Kishore, & Talukdar, 2016 ; Xu & Yano, 2017 ). Anticorruption strategies necessitate that managers engage in more strategic thinking and planning (Bahoo, Alon, & Paltrinieri, 2020 ). They will obtain more discretion over business operations (Owusu et al., 2019 ). Therefore, after firm exposure to anticorruption enforcement, firms may rely more on internal resource and capability building and thus may make more internal attribution for financial outcomes. Firms thus may pivot their focus toward efficiency logic (Kong et al., 2020 , 2022 ; Zhou, Gao, & Zhao, 2017 ), and the board may have strong motivation to attach a CEO’s effort to performance (Fang et al., 2023 ; Kong et al., 2022 ). As such, we propose the following hypothesis: Hypothesis 1 Exposure to anticorruption enforcement in a firm’s location is positively associated with the firm's CEO PPS. Moderating Role of SOEs The distinction between SOEs and private firms is quite pronounced, primarily rooted in their differing objectives, management structures, and innovation pursuits. SOEs serve both economic and political goals such as preserving employment, ensuring social stability, fulfilling national strategic goals (Du, Tang, & Young, 2012 ; Wang & Luo, 2019 ). In contrast, non-SOEs are generally profit-oriented, striving to increase shareholder value (Liu & Tian, 2012 ; Renneboog, Simons, & Wright, 2007 ). Studies also show that SOEs are typically more bureaucratic with operational decisions often influenced by government political cycles, and non-SOEs often have more streamlined management structures characterized by flexibility and market responsiveness (Chen, Sun, Tang, & Wu, 2011 ). In our context of firm exposure to anticorruption enforcement, we expect that SOEs are less affected by ousted corrupt officials than non-SOEs, and thus propose that the positive relationship between firm exposure to anticorruption enforcement and CEO PPS is less pronounced for SOEs. First, compared with SOEs, non-SOEs are more subject and sensitive to the political intervention. The rent-seeking theory (Chen et al., 2011 ) posits that private firms have stronger motivation to seek benefits from the political system through various means including lobbying. Second, private firms face serious industry entry restriction, higher capital and energy costs, and greater difficulty in obtaining approval for business projects (Gao & Yang, 2021 ). Such institutional constraints make harder for them to achieve a good performance or even make a survival. Compared with SOEs, non-SOEs may be more dependent on resources and support offered by politicians (Cao, Pan, Qian, & Tian, 2017 ; Liu, Luo, & Tian, 2016 ). Thus, ousted corrupt officials will result in a larger disruption for non-SOEs than SOEs (Kong et al., 2017; Pan & Tian, 2020 ), and non-SOEs’ responses will be more acute. We, therefore, suggest the following hypothesis: Hypothesis 2 The positive relationship between exposure to anticorruption enforcement in a firm’s location and a firm’s CEO PPS will be weaker for SOEs. Moderating Role of ETC To secure favorable treatment and gain a competitive edge, managers are highly motivated to foster close ties with the government (Cai et al., 2011 ). Recent research has highlighted the significant role of ETC as a strategic investment by firms in nurturing these relationships, which typically manifests as hosting and entertaining politicians, aiming to secure protection and support from the government (Cai et al., 2011 ; Chen et al., 2013 ; Fang et al., 2023 ). We expect that firms with higher ETC will be more motivated to adapt their attribution to CEO PPS in accordance with the power shifts and staff turnover caused by ousted corrupt officials. Firms often engage in relationship-building activities, including entertainment and travel with government officials, to secure favorable policies and business opportunities (Li, Meng, Wang, & Zhou, 2008 ). However, such practices have been directly targeted by recent anticorruption efforts (Zhang, 2018 ). This shift challenges the established mechanisms firms use to navigate the business-government interface in China, requiring a strategic reorientation. We suggest that such strategic reorientation occurs with a shift from government-centric to market-centric and firms with higher ETC will feel more urgent to focus on the internal development as well as internal attribution of firm financial performance (Fang et al., 2023 ; Kong et al., 2022 ). Moreover, after the corrupt officials are ousted in the aftermath of anticorruption shocks, other sitting officials commonly strive to cleanse the influence of their corrupt peers in public and private sectors in order to show their determination to fight corruption (Jiang et al., 2021 ; Pan & Tian, 2020 ). Simultaneously, successor officials, aiming to minimize the negative legacies of their predecessors, tend to marginalize firms associated with the ousted officials (Pan & Tian, 2020 ). Also, firms relying more on ETC before anticorruption may feel more pressure to attract talented leaders, thereby setting a higher PPS (Falato, Li, & Milbourn, 2015 ). We thus expect that: Hypothesis 3 The positive relationship between exposure to anticorruption enforcement in a firm’s location and a firm's CEO PPS will be stronger for a firm with larger ETC. Moderating Role of The Embeddedness of Ousted Corrupt Officials Within the Chinese state bureaucracy, social relations have long been essential, not only for carrying out one's assigned tasks but also for advancing one's career (Zhou, Ai, & Lian, 2012 ). The political embeddedness of government officials contributes to connections between officials and local enterprises, and lead to an increase in corporate rent-seeking behavior (Murphy, Shleifer, & Vishny, 2008 ; Sun, Mellahi, & Thun, 2010 ). In our context of ousted corruption officials, we suggest that the influence of firm exposure to anticorruption enforcement on CEO PPS may vary with political embeddedness of ousted corrupt officials. If the ousted corrupt official had a longer tenure in the province, it indicates there will be a more severe redistribution of political powers. Firms located in this area may face more challenges in reorienting their strategies and business operations. Firms will feel more pressure to cut off the connections with the local political system and it's also more challenging form them to rebuild the relationship with the incoming political officials (Jiang et al., 2021 ). Especially, the anticorruption invokes the strong monitoring on the government-business relationships (Griffin et al., 2022 ). To respond to these pressures, firms and their boards will be more responsive to the occurrence of outdated corrupt officials with longer tenure and are eager to shift their attention to merit-based criteria and corporate internal development to a larger extent, thereby increasing the sensitivity of CEO pay and corporate performance. We thus expect that: Hypothesis 4 The positive relationship between exposure to anticorruption enforcement in a firm’s location and a firm's CEO PPS will be stronger when ousted officials have larger local embeddedness. METHOD Data and Sample China is an appropriate empirical context for this study because of pervasive national anticorruption campaigns that Chinese president Xi Jinping launched the Campaign at the 18th People’s National Congress in November 2012. After anticorruption campaign, 110 top provincial officials (province level and deputy-province level) across 31 provinces and removed them from their posts between 2012 and 2018. Table 1 summarizes name of officials removed, the year, and province based on data from the website of CCDI ( www.ccdi.gov.cn/scdc ). Table 1 Removal of Top Provincial Officials on Corruption Charges During the Anticorruption Campaign Name of Official Year Province Name of Official Year Province Name of Official Year Province Bo Xilai 2012.04 Chongqing Sui Fengfu 2014.11 Heilongjiang Huang Xingguo 2016.09 Tianjin Li Chuncheng 2012.12 Sichuan Han Xuejian 2014.12 Heilongjiang Chen Shulong 2016.11 Anhui Ni Fake 2013.01 Anhui Yang Weize 2015.01 Jiangsu Wu Tianjun 2016.11 Henan Wang Suyi 2013.06 Inner Mongolia Lu Wucheng 2015.01 Gansu Zhang Wenxiong 2016.11 Hunan Guo Yongxiang 2013.06 Sichuan Xu Aimin 2015.02 Jiangxi Yu Haiyan 2017.01 Gansu Li Daqiu 2013.07 Guangxi Si Xinliang 2015.02 Zhejiang Li Wenke 2017.02 Liaoning Liao SHaohua 2013.01 Guizhou Li Zhi 2015.03 Xinjiang Chen Xu 2017.03 Shanghai Chen Baihuai 2013.11 Hubei Xu Gang 2015.03 Fujian Yang Chongyong 2017.03 Hebei Guo Youming 2013.11 Hubei Yan Shiyuan 2015.05 Shandong Zhou Chunyu 2017.04 Anhui Li Chongxi 2013.12 Sichuan Han Zhiran 2015.06 Inner Mongolia Wang Hongjiang 2017.05 Tianjin Chen Anzhong 2013.12 Jiangxi Le Dake 2015.06 Xizang Zeng Zhiquan 2017.05 Guangdong Tong Mingqian 2013.12 Hunan Liu Zhiyong 2015.07 Guangxi Wei Minzhou 2017.05 Shaanxi Jin Daoming 2014.02 Shanxi Zhou Benshun 2015.07 Hebei Ai Wenli 2017.06 Hebei Ji Wenlin 2014.02 Hainan Gu Chunli 2015.08 Jilin He Ting 2017.06 Chongqing Zu Zuoli 2014.02 Shaanxi Sun Qingyun 2015.09 Shaanxi Xu Qianfei 2017.07 Jiangsu Shen Peiping 2014.03 Yunnan Su Shulin 2015.01 Fujian Wang Sanyun 2017.07 Gansu Yao Mugen 2014.03 Jiangxi Ai Baojun 2015.11 Shanghai Sun Zhengcai 2017.07 Chongqing Jing Chunhua 2014.03 Hebei Lv Xiwen 2015.11 Beijing Mu Huaping 2017.01 Chongqing Mao Xiaobing 2014.04 Qinghai Bai Xueshan 2015.11 Ningxia Liu Qiang 2017.11 Liaoning Yang Baohua 2014.05 Hunan Cao Jianfang 2015.12 Yunnan Ji Xiangqi 2018.01 Shandong Tan Xiwei 2014.05 Chongqing Wi Hong 2015.12 Sichuan Li Yihuang 2018.01 Jiangxi Du Shanxue 2014.06 Shanxi Liu Lizu 2015.12 Jiangxi Feng Xinzhu 2018.01 Shaanxi Ling Zhengce 2014.06 Shanxi Gai Ruyin 2015.12 Heilongjiang Liu Jun 2018.02 Guangxi Zhao Zhiyong 2014.06 Jiangxi Liu Zhigeng 2016.02 Guangdong Bai Xiangqun 2018.04 Inner Mongolia Qiu He 2014.07 Yunnan Lu Ziyue 2016.02 Zhejiang Wang Xiaoguang 2018.04 Guizhou Wu Changshun 2014.07 Tianjin He Jiatie 2016.02 Hubei Pu Bo 2018.05 Guizhou Han Xiancong 2014.07 Anhui Zhang Lifu 2016.03 Hainan Jin YUhui 2018.08 Jilin Tan Li 2014.07 Hainan Wang Yang 2016.03 Liaoning Li JInxiu 2018.08 Jilin Chen Tiexin 2014.07 Liaoning Li Chengyun 2016.04 Sichuan Wang Erzhi 2018.08 Jilin Chen Chuanping 2014.08 Shanxi Zhang Yue 2016.04 Hebei Chen Zhifeng 2018.08 Tianjin Bai Yun 2014.08 Shanxi Kong Lingzhong 2016.04 Guizhou Wang Tie 2018.08 Henan Nie Chunyu 2014.08 Shanxi Su Hongzhang 2016.04 Liaoning Li Shixiang 2018.09 Beijing Ren Runhou 2014.08 Shanxi Yang Zhenchao 2016.05 Anhui Jin Suidong 2018.09 Henan Oan Yiyang 2014.09 Inner Mongolia Li Yunfeng 2016.05 Jiangsu Xin Yun 2018.01 Inner Mongolia Qin Yuhai 2014.09 Henan Lai Derong 2016.06 Guangxi Qian Yinan 2018.01 Shaanxi Zhu Mingguo 2014.11 Guangdong Yin Hailin 2016.08 Tianjin Miao Ruilin 2018.11 Jiangsu Liang Bin 2014.11 Hebei Zheng Yuzhuo 2016.08 Liaoning Notes. This table summarizes the removal of top provincial officials on corruption charges after the inception of the anticorruption campaign. Included are removals of province level and deputy-province level officials. The data are compiled from the website of the Central Commission for Discipline Inspection. ---Insert Table 1 about here--- We exacted information from multiple data sources to construct our dataset. First, we collected the resumes of all ousted officials at deputy-province level and province level from the CCDI website, including the official’s birth year, positions and ranking of indicted corrupt officials, and the specific time of their removal. Second, the sample we use in this study is all listed firms in Shanghai Stock Exchange and Shenzhen Stock Exchange. Firm-level information comes from the China Stock Market and Accounting Research (CSMAR) database and the WIND database. Based on the described procedure and after merging with the other databases, our final main sample consists of 24,734 firm-year observations for 3,215 unique firms. Measurements Dependent variable (Industry-adjusted CEO pay). According to Shi, Connelly, Mackey, and Gupta ( 2019 ), the dependent variable in our study is total CEO pay, which consists of salary, bonuses, the value of restricted stock granted, the value of options granted, long-term incentive payouts, and other compensation, which equals the natural logarithm of CEO total pay to fit a log-linear model (Banker, Darrough, Huang, & Plehn-Dujowich, 2013 ). We adjust CEO pay by the average of compensation levels of other CEOs in the same industry (based on three-digit SIC codes) to partial out the differences in CEO compensation across industries (Shi et al., 2019 ). Independent variable (industry-adjusted ROA). For our independent variable, firm performance, we use industry-adjusted return on assets (ROA) , which equals the difference between a focal firm's ROA and the average of ROA of other firms in the same three-digit industry (Chen, Chittoor, & Vissa, 2021 ). Particularly, stock options are a rarely used element of CEO compensation in Chinese stock markets, we do not use share price performance or change in shareholder wealth to measure firm performance. Moderators. Our primary moderator is firm exposure to anticorruption enforcement . This variable is the removal of a top provincial official on corruption charges, which equaled one for the three-year period after the event and zero otherwise (Fang, Lerner, Wu, & Zhang, 2023 ). We also consider three additional moderators that can affect the role of firm exposure to anticorruption enforcement in shaping the PPS. The first is SOEs , which is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders (Marquis & Qian, 2014 ; Wang, Wong, & Xia, 2008 ). The second additional moderator is ETC . According to Fang et al. ( 2023 ), we first estimate a regression model in which the dependent variable is the entertainment expenses divided by sales revenues. The predictor variables in the model include total sales, total assets, and the log of per capita GDP of the firm’s home province. We then use the residual from this regression model as a proxy for abnormal entertainment expenses, which reflects a firm’s relationship expenditure with government. The third moderator is political embeddedness of ousted corrupt officials , which equals to the time that corrupt officials have served in provincial government departments in this province (e.g., Lester, Hillman, Zardkoohi, & Cannella, 2008 ). Control variables. We control for several variables that are expected to have an impact on CEO compensation design, including firm size, firm age, leverage, board size, board independence, R&D, CEO duality, state monopoly, firm dependence government, political contestability , and GDP (e.g., Chen et al., 2021 ; Fang et al., 2023 ; Shi et al., 2019 ; Jiang et al., 2021 ; Zhang, Marquis, & Qiao, 2016 ). We controlled for province-, industry- and year-fixed effects in the regressions to account for unobserved systematic heterogeneities across different firms and years, respectively. We present detailed definitions in Table 2 . Table 2 Variable Definitions Variables Definitions Firm age The natural log of the number of years since the firm was founded Firm size The natural log of total assets Leverage The ratio of the total debt to total assets Board size The natural log of number of board directors Board independence The percentage independent directors on the boards R&D The total R&D expenditure scaled by revenue CEO duality Equals to one if the CEO is also the chairman of the firm, and zero otherwise State monopoly The proportion of SOEs’ sales to those of the total industry (at three-digit level) for each year Firm dependence government The ratio of the government subsidies divided by the firm’s annual operating income Political contestability The ousted corrupt official’s prior political appointments, calculating the total number of different provinces in which they had served as province level and deputy-province level official prior to their current positions in the focal province GDP GDP of the province where the focal firms are headquartered SOEs SOEs is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders ETC The residual of the ETC divided by sales revenues Political embeddedness The time that corrupt officials have served in provincial government departments in this province Firm exposure to anticorruption enforcement The removal of a top provincial official on corruption charges, which equaled one for the three-year period after the event and zero otherwise Industry-adjusted ROA The difference between a focal firm's ROA and the average of ROA of other firms in the same three-digit industry Industry-adjusted CEO pay The difference between a focal firm's CEO pay and the average of CEO pay of other firms in the same three-digit industry ---Insert Table 2 about here--- RESULTS Main Effects Table 3 reports the summary statistics for our sample, which consists of annual firm-level observations. Table 4 presents the results of Pearson correlation tests among the main variables in our sample. Firm-level industry-adjusted ROA was highly correlated with industry-adjusted CEO pay. To check the potential multicollinearity in our analyses, we calculated variance inflation factors (VIFs) for all our estimation models. Results show that the average VIF value was 1.46 (the maximum VIF was 2.31), indicating no significant concern about multicollinearity. Table 3 Summary Statistics for All Variables Variables Mean SD Min P25 P50 P75 Max Firm age 2.977 0.279 2.197 2.773 2.996 3.178 3.611 Firm size 22.04 1.299 19.39 21.1 21.88 22.8 26.05 Leverage 0.430 0.217 0.048 0.255 0.419 0.592 0.971 Board size 8.694 1.717 4 7 9 9 18 Board independence 0.372 0.054 0 0.333 0.333 0.429 0.8 R&D 3.315 4.079 0 0.095 2.64 4.51 22.37 CEO duality 0.261 0.439 0 0 0 1 1 State monopoly 0.539 0.265 0.001 0.334 0.519 0.748 1 Firm dependence government 0.039 0.072 0 0.003 0.013 0.039 0.453 Political contestability 0.253 0.625 0 0 0 0 4 GDP 10.35 0.796 6.100 9.904 10.39 10.95 11.59 SOEs 0.384 0.486 0 0 0 1 1 ETC 0 0.006 -0.017 -0.003 -0.001 0.002 0.043 Political embeddedness 2.307 1.359 0 1.792 2.944 3.332 3.714 Firm exposure to anticorruption enforcement 0.516 0.5 0 0 1 1 1 Industry-adjusted ROA 0.003 6.528 -27.75 -2.941 -0.207 3.142 19.05 Industry-adjusted CEO pay -0.427 2.594 -5.097 -2.415 -0.838 1.168 6.823 Notes. The table reports summary statistics of the main variables in the listed family-firm sample for the period of 2009–2018. N = 24,734. All financial amounts are measured in millions of RMB. All financial ratios are calculated annually using data from firms’ annual statements. Table 4 Correlations between All Variables Variables 1 2 3 4 5 6 7 8 1 Firm age 1 2 size 0.170*** 1 3 leverage 0.170*** 0.453*** 1 4 Board size 0.019*** 0.275*** 0.161*** 1 5 Board independence -0.027*** 0.012* -0.008 -0.453*** 1 6 R&D -0.090*** -0.230*** -0.361*** -0.147*** 0.067*** 1 7 CEO duality -0.083*** -0.174*** -0.162*** -0.171*** 0.092*** 0.186*** 1 8 State monopoly -0.101*** 0.229*** 0.192*** 0.237*** -0.071*** -0.296*** -0.162*** 1 9 Firm dependence government -0.044*** -0.074*** -0.130*** 0.004 -0.001 0.096*** 0.026*** -0.017*** 10 Political contestability 0.086*** -0.016** -0.018*** -0.013** 0.024*** 0.033*** 0.038*** -0.024*** 11 GDP 0.181*** -0.006 -0.137*** -0.133*** 0.027*** 0.204*** 0.142*** -0.249*** 12 SOEs 0.083*** 0.348*** 0.314*** 0.276*** -0.051*** -0.295*** -0.297*** 0.382*** 13 ETC 0.025*** 0.006 0.060*** 0.009 0.010 0.065*** -0.019*** -0.006 14 Official tenure 0.212*** 0.068*** -0.049*** -0.084*** 0.031*** 0.131*** 0.044*** -0.150*** 15 Firm exposure to anticorruption enforcement 0.303*** 0.106*** -0.060*** -0.100*** 0.041*** 0.141*** 0.058*** -0.203*** 16 Industry-adjusted ROA -0.078*** 0.050*** -0.263*** 0.006 -0.019*** 0.012* 0.044*** 0.004 17 Industry-adjusted CEO pay -0.170*** -0.045*** -0.218*** -0.034*** 0.005 0.170*** 0.333*** 0.049*** 9 10 11 12 13 14 15 16 9 Firm dependence government 1 10 Political contestability 0.006 1 11 GDP -0.066*** 0.127*** 1 12 SOEs -0.093*** -0.055*** -0.255*** 1 13 ETC 0.096*** -0.011* -0.005 -0.050*** 1 14 Official tenure -0.039*** 0.071*** 0.293*** -0.091*** 0.004 1 15 Firm exposure to anticorruption enforcement -0.069*** 0.085*** 0.324*** -0.113*** -0.003 0.613*** 1 16 Industry-adjusted ROA 0.055*** -0.002 0.084*** -0.101*** -0.025*** -0.007 -0.005 1 17 Industry-adjusted CEO pay 0.066*** 0.026*** 0.144*** -0.293*** -0.014** -0.008 -0.001 0.261*** Notes. N = 24,734. This table presents the Pearson correlation matrix among the main variables in our analysis. *, **, ***Statistical significance at the 10%, 5%, and 1% levels, respectively. ---Insert Tables 3 and 4 about here--- Table 5 reports the regression results for the removal of corrupt officials. Model 1 focuses on the key independent variables: Industry-adjusted ROA and Firm exposure to anticorruption enforcement . The main effect of industry-adjusted ROA is positive ( β = 0.103, p = 0.000), as expected, because CEOs receive higher compensation when the firm's ROA is performing well. Hypothesis 1 predicts that after anticorruption shocks, firms located in province with ousted corrupt officials will be motivated to increase CEO PPS. In Model 2, we add the interaction term between industry-adjusted ROA and firm exposure to anticorruption enforcement . The result shows a positive and statistically significant coefficient ( β = 0.024, p = 0.000) for Firm exposure to anticorruption enforcement × Industry-adjusted ROA , in the absence of any controls. Model 3 of Table 5 reports the full model controlling for time-varying firm characteristics. Model 4 adds province fixed effects, and Model 5 includes year and firm fixed effects. We continue to find positive and significant coefficients on the interaction term between industry-adjusted ROA and firm exposure to anticorruption enforcement across these three models, thereby supporting Hypothesis 1 . Table 5 Baseline Models: Firm Exposure to Anticorruption Enforcement and CEO Pay-for-Performance Sensitivity Variables Model 1 Model 2 Model 3 Model 4 Model 5 Firm age -1.003 *** -1.002 *** -2.203 *** (0.059) (0.060) (0.262) Firm size 0.174 *** 0.161 *** 0.335 *** (0.014) (0.014) (0.023) Leverage -1.200 *** -1.153 *** -0.827 *** (0.084) (0.085) (0.092) Board size 0.056 *** 0.056 *** 0.038 ** (0.010) (0.010) (0.012) Board independence -0.453 -0.549 + -0.273 (0.297) (0.297) (0.308) R&D 0.077 *** 0.074 *** 0.004 (0.004) (0.004) (0.005) CEO duality 1.553 *** 1.524 *** 0.741 *** (0.034) (0.034) (0.033) State monopoly 1.753 *** 1.799 *** 1.150 *** (0.070) (0.070) (0.162) Firm dependence government 0.587 ** 0.561 ** 0.284 + (0.202) (0.202) (0.166) Political contestability 0.023 0.026 0.025 (0.023) (0.026) (0.017) GDP 0.281 *** 0.511 * 0.266 * (0.021) (0.206) (0.135) SOEs -1.336 *** -1.318 *** -0.541 *** (0.035) (0.036) (0.073) ETC -6.621 ** -6.179 ** 0.986 (2.331) (2.337) (2.324) Political embeddedness -0.012 0.018 -0.008 (0.018) (0.019) (0.013) Firm exposure to anticorruption enforcement -0.113 + -0.111 + -0.078 -0.031 -0.003 (0.060) (0.060) (0.061) (0.063) (0.040) Industry-adjusted ROA 0.103 *** 0.090 *** 0.054 *** 0.054 *** 0.027 *** (0.002) (0.004) (0.003) (0.003) (0.002) Firm exposure to anticorruption enforcement × Industry-adjusted ROA 0.024 *** 0.028 *** 0.028 *** 0.008 ** (0.005) (0.004) (0.004) (0.003) Year FE YES YES YES YES YES Industry FE YES YES YES YES NO Province FE NO NO NO YES NO Firm FE NO NO NO NO YES Constant -0.369 *** -0.369 *** -5.034 *** -7.235 *** -4.495 ** (0.035) (0.035) (0.410) (2.167) (1.657) adj. R 2 0.073 0.074 0.287 0.293 0.721 F 905.328 612.474 576.505 496.641 90.451 Notes. N = 24,734. This table reports results of panel regressions of PPS before and after the removal of top provincial officials on corruption charges. The dependent variable is Industry-adjusted CEO pay, measured as the difference between a focal firm's CEO pay and the average of CEO pay of other firms in the same three-digit industry. Detailed variable definitions are found in Table 2 . In columns (1) and (2), only controls for industry and year dummies are included. Columns (3)–(5) present the results that include control variables. Standard errors are in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 5 about here--- Furthermore, we calculate the magnitude of the interaction effect when firm exposure to anticorruption enforcement and industry-adjusted ROA take different values. We define high (low) values of industry-adjusted ROA as the mean plus (minus) one standard deviation, and calculate the marginal effects of industry-adjusted ROA on industry-adjusted CEO pay when firm exposure to anticorruption enforcement takes different values. We present these results in Fig. 1 . When firm exposure to anticorruption enforcement is zero (with all other variables set to their mean values), industry-adjusted CEO pay increases by 92.89% when industry-adjusted ROA increases from its low to high value. When firm exposure to anticorruption enforcement is one, industry-adjusted CEO pay increases by 109.42% for the same increase in industry-adjusted ROA. Therefore, PPS is greater after a corrupt official’s downfall, indicating that board rewards CEO more for strong firm performance and penalize them more for poor firm performance after anticorruption shocks. ---Insert Fig. 1 about here--- Moderating Effects Our moderating hypotheses (Hypotheses 2, 3, and 4) predict that the impact of firm exposure to anticorruption enforcement on PPS is moderated by SOEs, ETC, and Political embeddedness. Table 6 reports the results for these three hypotheses. Models 1–3 report the three-way interactions between industry-adjusted ROA , firm exposure to anticorruption enforcement , and the three moderators respectively. Model 4 is the full model, including all non-interacted effects and interaction terms. Table 6 Moderating Analysis Variables Model 1 Model 2 Model 3 Model 4 Firm age -0.980 *** -1.002 *** -1.000 *** -0.978 *** (0.060) (0.060) (0.060) (0.060) Firm size 0.168 *** 0.161 *** 0.161 *** 0.168 *** (0.014) (0.014) (0.014) (0.014) Leverage -1.205 *** -1.148 *** -1.150 *** -1.198 *** (0.085) (0.085) (0.085) (0.085) Board size 0.057 *** 0.057 *** 0.056 *** 0.057 *** (0.010) (0.010) (0.010) (0.010) Board independence -0.549 + -0.552 + -0.549 + -0.556 + (0.297) (0.297) (0.297) (0.297) R&D 0.074 *** 0.074 *** 0.074 *** 0.074 *** (0.004) (0.004) (0.004) (0.004) CEO duality 1.517 *** 1.523 *** 1.525 *** 1.517 *** (0.034) (0.034) (0.034) (0.034) State monopoly 1.822 *** 1.799 *** 1.799 *** 1.822 *** (0.070) (0.070) (0.070) (0.070) Firm dependence government 0.571 ** 0.547 ** 0.564 ** 0.559 ** (0.202) (0.202) (0.202) (0.202) Political contestability 0.024 0.026 0.029 0.027 (0.026) (0.026) (0.027) (0.027) GDP 0.514 * 0.505 * 0.513 * 0.511 * (0.206) (0.206) (0.208) (0.208) SOEs -1.224 *** -1.317 *** -1.317 *** -1.220 *** (0.046) (0.036) (0.036) (0.046) ETC -6.106 ** -6.603 * -6.212 ** -6.353 * (2.337) (3.114) (2.336) (3.109) Political embeddedness 0.016 0.018 0.015 0.013 (0.019) (0.019) (0.021) (0.021) Firm exposure to anticorruption enforcement 0.053 -0.028 -0.084 0.005 (0.067) (0.063) (0.168) (0.169) Industry-adjusted ROA 0.065 *** 0.055 *** 0.049 *** 0.061 *** (0.004) (0.003) (0.004) (0.005) Firm exposure to anticorruption enforcement × Industry-adjusted ROA 0.026 *** 0.026 *** -0.020 -0.024 (0.005) (0.004) (0.020) (0.020) SOEs × Firm exposure to anticorruption enforcement -0.242 *** -0.247 *** (0.059) (0.059) SOEs × Industry-adjusted ROA -0.027 *** -0.028 *** (0.006) (0.006) SOEs × Firm exposure to anticorruption enforcement × Industry-adjusted ROA -0.019 * -0.017 + (0.009) (0.009) ETC × Firm exposure to anticorruption enforcement 0.208 -1.007 (4.740) (4.740) ETC × Industry-adjusted ROA -0.897 ** -1.037 ** (0.341) (0.341) ETC × Firm exposure to anticorruption enforcement × Industry-adjusted ROA 1.122 * 1.044 * (0.510) (0.511) Political embeddedness × Firm exposure to anticorruption enforcement 0.019 0.019 (0.054) (0.054) Political embeddedness × Industry-adjusted ROA 0.004 + 0.005 * (0.002) (0.002) Political embeddedness × Firm exposure to anticorruption enforcement × Industry-adjusted ROA 0.013 * 0.013 + (0.007) (0.007) Industry/Year/Province FE YES YES YES YES Constant -7.515 *** -7.180 *** -7.249 *** -7.496 *** (2.165) (2.167) (2.189) (2.186) adj. R 2 0.295 0.293 0.294 0.296 F 427.037 422.589 422.832 329.523 Notes. N = 24,734. This table reports results of moderating analysis. SOEs is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders. ETC is the residual of abnormal entertainment expenses, which reflects a firm’s relationship expenditure with government. Political embeddedness equals to the time that corrupt officials have served in provincial government departments in this province. Standard errors in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 6 about here--- Hypothesis 2 predicts that SOEs further moderate the interaction described in Hypothesis 1 , such that the relationship is weaker for SOEs. Model 1 of Table 6 shows that the coefficient estimate of SOEs × Firm exposure to anticorruption enforcement × Industry-adjusted ROA is negative ( β = − 0.019, p = 0.048), thus supporting Hypothesis 2 . To gain a better understanding of the moderating effect of SOEs, we illustrate the three-way interaction in Fig. 2 . We observe that the slope of the solid line is steeper on the left side of Fig. 2 than on right side (firm exposure to anticorruption enforcement equals one), while the slope of the dotted line is flatter on the left than on the right (firm exposure to anticorruption enforcement equals zero), indicating that the role of firm exposure to anticorruption enforcement in shaping PPS is stronger in the presence of non-SOEs. ---Insert Fig. 2 about here--- Hypothesis 3 proposes that ETC also moderate the interaction described in Hypothesis 1 , such that the relationship is stronger when ETC is high. Model 2 of Table 6 shows that the coefficient estimate of ETC × Firm exposure to anticorruption enforcement × Industry-adjusted ROA is positive ( β = 1.122, p = 0.028), thus supporting Hypothesis 3 . Figure 3 further illustrates a steeper effect under high ETC conditions. ---Insert Fig. 3 about here--- Hypothesis 4 proposes that political embeddedness also moderate the interaction described in Hypothesis 1 , such that the relationship is stronger when political embeddedness is high. Model 3 of Table 6 shows that the coefficient estimate of Political embeddedness × Firm exposure to anticorruption enforcement × Industry-adjusted ROA is positive ( β = 0.013, p = 0.048), thus supporting Hypothesis 4 . Figure 4 further demonstrates a stronger effect under conditions of high political embeddedness. ---Insert Fig. 4 about here--- Robustness Tests and Supplementary Analyses We conducted a series of additional analyses to further assess the robustness of the above findings. These include parallel trend analysis, placebo tests, propensity-score-match, alternative measures of dependent variables, and alternative measures of anticorruption shocks. Parallel Trend Analysis We perform a parallel trend test to analyze the response of PPS around the timing of ousted corrupt officials during anticorruption shocks. Our method assumes no prior differences in PPS between affected and unaffected firms, aside from anticorruption actions. Therefore, we introduce dummy variables to indicate the periods of ousted corrupt officials during anticorruption shocks. where k (k=-3, -2, -1, 0, 1, 2, 3) denotes the pre-period and after-period of firm exposure to anticorruption enforcement . These seven dummy variables capture the dynamic effects of firm exposure to anticorruption enforcement on PPS. Controls denote the set of basic variables. The results in Table 7 show that the coefficient of Firm exposure to anticorruption enforcement × Industry-adjusted ROA (for k = 0, 1, 2, 3) are positively significant, whereas those of before firm exposure to anticorruption enforcement (for k=-1, -2, -3) are insignificant. These estimates demonstrate that anticorruption shocks have deferred positive effects on PPS. Table 7 Parallel Trend Tests (1) Control variables Controlled Industry-adjusted ROA 0.057 *** (0.003) Before [-3] FEAE 0.050 (0.091) Before [-2] FEAE -0.003 (0.096) Before [-1] FEAE -0.010 (0.098) Current FEAE -0.001 (0.096) After [+ 1] FEAE 0.020 (0.091) After [+ 2] FEAE 0.011 (0.080) After [+ 3] FEAE -0.006 (0.072) Industry-adjusted ROA × Before [-3] FEAE 0.008 (0.009) Industry-adjusted ROA × Before [-2] FEAE -0.005 (0.009) Industry-adjusted ROA × Before [-1] FEAE 0.009 (0.009) Industry-adjusted ROA × Current FEAE 0.021 * (0.008) Industry-adjusted ROA × After [+ 1] FEAE 0.033 *** (0.008) Industry-adjusted ROA × After [+ 2] FEAE 0.021 ** (0.007) Industry-adjusted ROA × After [+ 3] FEAE 0.033 *** (0.007) Industry/Year/Province FE YES Constant -6.440 ** (2.007) adj. R 2 0.293 F 121.549 Notes. N = 24,734. The table considers PPS for three years pre- and post- firm exposure to anticorruption enforcement (FEAE). Standard errors in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 7 about here--- Placebo Tests While we observe a positive impact of removing corrupt officials on PPS, this effect could be coincidental due to potential selection biases. To address this concern, we employ placebo tests within China's anticorruption campaign. By simulating firm exposure to anticorruption enforcement under counterfactual conditions, we evaluate if positive effects persist, indicating influence from unobservable systemic factors rather than anticorruption actions alone. We enhance the estimation power through conducting 500 simulated placebo tests involving random timings and regions of official downfalls. We graph the p-values and regression coefficients of 500 fictitious regressions in a probability density plot (Fig. 5 ), revealing that the coefficients cluster around zero and approximate a normal distribution. This suggests that random factors do not significantly affect our results. Additional analysis shows that only 11 out of 500 spurious coefficients are statistically significant compared to the benchmark, confirming the robustness of our findings with a 97.8% certainty. These placebo tests underscore that PPS's response to anticorruption shocks is not due to chance, reinforcing the significant impact of firm exposure to anticorruption enforcement. ---Insert Fig. 5 about here--- Using Propensity Score to Identify Comparable Firms Using a multiple regression model, we find that the removal of officials influences PPS, but covariate variables could weaken exchangeability between CEOs of affected and unaffected firms, thereby introducing estimation bias. To overcome this issue in our non-randomized setting, we apply the propensity-score matching (PSM) method to control for confounders and better estimate the effects of firm exposure to anticorruption enforcement. We match firms based on similar propensity scores derived from a probit regression model, which considers firm and regional characteristics before the anticorruption campaign. The pairs are chosen from provinces where officials were removed (Beijing, Shanghai, Ningxia) in November 2015 and matched with firms from other provinces for comparison. This procedure was conducted annually from 2009 to 2014. The second stage involves comparing matched firms annually, ensuring each affected firm was paired with an unaffected one with a similar propensity score. The result presented in Table 8 shows a significantly positive coefficient on the interaction term Post × Treated × Industry-adjusted ROA ( β = 0.026, p < 0.001) which confirms our baseline finding and indicates a reliable impact of anticorruption enforcement on PPS. Table 8 PSM-DID (1) Firm age -0.798 * (0.386) Firm size 0.241 *** (0.039) Leverage 0.347 * (0.142) Board size 0.040 * (0.017) Board independence 0.066 (0.414) R&D -0.001 (0.007) CEO duality 0.358 *** (0.048) State monopoly 1.319 *** (0.262) Firm dependence government 0.428 + (0.230) Political contestability 0.079 (0.073) GDP -0.519 ** (0.197) SOEs 0.018 (0.104) ETC 18.709 *** (2.936) Political embeddedness 0.001 (0.016) Industry-adjusted ROA 0.035 *** (0.004) Post -0.185 *** (0.036) Treated -0.075 (0.348) Post × Treated 0.182 *** (0.047) Post × Industry-adjusted ROA -0.016 ** (0.005) Treated × Industry-adjusted ROA -0.002 (0.006) Post × Treated × Industry-adjusted ROA 0.026 *** (0.007) Industry/Year/Province FE YES Constant 0.440 (1.545) adj. R 2 -0.123 F 30.320 N 13450 Note. N = 13,450. This table presents examine the impacts of firm exposure to anticorruption enforcement on PPS through PSM method. The dependent variable is Industry-adjusted CEO pay. The treated groups are firms whose headquarters are located in provinces for firm exposure to anticorruption enforcement before the year 2015, and the control groups are firms whose headquarters are located in Beijing, Shanghai, and Ningxia provinces. Standard errors in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 8 about here--- Alternative Measures of Dependent Variables Our baseline results focus on CEO pay. For robustness tests, we alternatively use chairman pay and the combined total pay of the CEO and chairman. For chairman pay, we adjust it by the average of compensation levels of other chairman in the same industry (based on three-digit SIC codes) to partial out the differences in chairman compensation across industries ( Industry-adjusted chairman pay ). Meanwhile, Industry-adjusted total pay equals the difference between total compensation and average of compensation levels of other chairman and CEO in the same industry (based on three-digit SIC codes). The results presented in columns 1–2 of Table 9 show that the coefficients on the interaction term Firm exposure to anticorruption enforcement × Industry-adjusted ROA are both significantly positive ( β = 0.023, p < 0.001 in column (1), β = 0.028, p < 0.001 in column (2)), which are highly consistent with the main resluts. Table 9 Alternative Dependent Variables (1) (2) Industry-adjusted Chairman pay Industry-adjusted total pay Firm age -1.632 *** -1.480 *** (0.085) (0.065) Firm size 0.221 *** 0.223 *** (0.021) (0.015) Leverage -1.678 *** -1.470 *** (0.126) (0.092) Board size -0.012 0.007 (0.015) (0.011) Board independence -0.991 * -0.585 + (0.429) (0.322) R&D 0.087 *** 0.085 *** (0.006) (0.005) CEO duality 0.333 *** 0.569 *** (0.045) (0.037) State monopoly 1.831 *** 2.154 *** (0.100) (0.076) Firm dependence government 2.075 *** 1.716 *** (0.281) (0.219) Political contestability 0.079 * 0.063 * (0.037) (0.029) GDP 0.725 * 0.788 *** (0.311) (0.224) SOEs -2.500 *** -2.328 *** (0.054) (0.039) ETC -12.987 *** -9.825 *** (3.340) (2.535) Political embeddedness 0.040 0.033 (0.029) (0.021) Firm exposure to anticorruption enforcement -0.017 -0.038 (0.093) (0.068) Industry-adjusted ROA 0.064 *** 0.057 *** (0.005) (0.004) Firm exposure to anticorruption enforcement × Industry-adjusted ROA 0.023 *** 0.028 *** (0.006) (0.005) Industry/Year/Province FE YES YES Constant -7.622 * -9.075 *** (3.291) (2.351) adj. R 2 0.266 0.327 F 340.436 577.352 Notes. The table represents the results for chairman pay and total pay between chairman and CEO. Model 1 ( N = 19,568) show the result for industry-adjusted chairman pay, which is calculated by the difference between chairman compensation and average of compensation levels of other chairman in the same industry. Model 2 ( N = 24,734) show the result for industry-adjusted total pay, which is calculated by the difference between total compensation and average of compensation levels of other chairman and CEO in the same industry. Standard errors in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 9 about here--- Deterrence Effect of Anticorruption Shocks We hypothesize that the removal of top provincial officials on corruption charges prompts strategic changes in firms, but the anticorruption campaign itself might also have a deterrent effect before actual enforcement actions (Fang et al., 2023 ). We designate 2012 as the key year of the anticorruption shock and analyze subsidy decisions from three years before (2009–2011) to three years after (2013–2015). The variable "Post_2012" is set to one for the years 2013 to 2015 and zero for 2009 to 2011. Although this approach doesn’t perfectly isolate the shock effect, given potential concurrent events in China. Table 10 presents the results of this robust check, in column (2), the coefficeint on the interaction term Post_2012 × Industry-adjusted ROA is significantly positive ( β = 0.016, p < 0.05), suggesting that the anticorruption campaign's threat significantly influence CEO PPS. In column (3), the coefficeint on the interaction term SOEs ×Post_2012 × Industry-adjusted ROA is significantly negative ( β = -0.024, p < 0.1), further comfirming the moderating effect of SOEs, and the significantly positive coefficient on the interaction term ETC ×Post_2012 × Industry-adjusted ROA ( β = 1.754, p < 0.05) in column (4) affirms the moderating effect of ETC. Table 10 Deterrence Effect of Anticorruption Campaign Variables (1) (2) (3) (4) (5) Firm age -1.053 *** -1.055 *** -1.035 *** -1.057 *** -1.037 *** (0.090) (0.090) (0.095) (0.090) (0.090) Size 0.225 *** 0.226 *** 0.229 *** 0.225 *** 0.229 *** (0.023) (0.023) (0.021) (0.024) (0.024) Leverage -1.483 *** -1.486 *** -1.515 *** -1.469 *** -1.499 *** (0.134) (0.134) (0.129) (0.134) (0.134) Board size 0.069 *** 0.070 *** 0.069 *** 0.071 *** 0.070 *** (0.015) (0.015) (0.013) (0.015) (0.015) Board independence -0.545 -0.536 -0.594 -0.547 -0.607 (0.465) (0.465) (0.449) (0.465) (0.464) R&D 0.069 *** 0.070 *** 0.068 *** 0.069 *** 0.068 *** (0.008) (0.008) (0.009) (0.008) (0.008) CEO duality 1.454 *** 1.454 *** 1.452 *** 1.452 *** 1.449 *** (0.056) (0.056) (0.067) (0.056) (0.056) State monopoly 1.421 *** 1.422 *** 1.447 *** 1.420 *** 1.445 *** (0.114) (0.114) (0.121) (0.114) (0.114) Firm dependence government 0.738 * 0.742 * 0.765 * 0.719 * 0.741 * (0.313) (0.313) (0.341) (0.313) (0.313) Political contestability -0.002 -0.005 -0.036 -0.001 -0.031 (0.058) (0.058) (0.062) (0.058) (0.058) GDP -0.620 ** -0.605 ** -0.500 ** -0.616 ** -0.510 * (0.204) (0.204) (0.190) (0.204) (0.204) SOEs -1.675 *** -1.677 *** -1.387 *** -1.673 *** -1.380 *** (0.057) (0.057) (0.067) (0.057) (0.077) ETC -2.550 -2.469 -3.123 -8.984 + -9.406 + (3.521) (3.521) (3.584) (5.045) (5.040) Official tenure -0.017 -0.020 -0.038 -0.021 -0.039 (0.038) (0.038) (0.033) (0.038) (0.038) Post_2012 0.221 + 0.223 + 0.459 *** 0.231 + 0.471 *** (0.121) (0.121) (0.119) (0.121) (0.128) Industry-adjusted ROA 0.059 *** 0.051 *** 0.055 *** 0.053 *** 0.058 *** (0.004) (0.005) (0.008) (0.005) (0.007) Post_2012 × Industry-adjusted ROA 0.016 * 0.019 + 0.014 * 0.017 + (0.007) (0.011) (0.007) (0.009) SOEs ×Post_2012 -0.544 *** -0.552 *** (0.085) (0.094) SOEs × Industry-adjusted ROA -0.003 -0.003 (0.010) (0.010) SOEs ×Post_2012 × Industry-adjusted ROA -0.024 + -0.024 + (0.014) (0.015) ETC ×Post_2012 10.716 10.046 (7.037) (7.029) ETC × Industry-adjusted ROA -1.963 *** -2.052 *** (0.550) (0.550) ETC ×Post_2012 × Industry-adjusted ROA 1.754 * 1.776 * (0.775) (0.775) Industry/Province fixed effect YES YES YES YES YES Constant 4.509 * 4.342 * 3.090 + 4.471 * 3.206 (1.992) (1.993) (1.861) (1.992) (2.003) adj. R 2 0.268 0.268 0.270 0.268 0.271 F 73.274 72.218 76.728 69.227 67.007 Notes. N = 12,270. This table examines the deterrence effect of the anticorruption campaign. We examine subsidy granting decisions by repeating the regression of Tables 5 and 6 for various subsamples. We use 2012 (the start of the anticorruption campaign) as the cutoff year and focus on PPS made in the three years before (2009 to 2011) versus three years after (2013 to 2015). We estimate the regression for the whole sample. However, political embeddedness represents the characteristic of corrupted official, it thus doesn’t fit this model. Standard errors are in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 10 about here--- Channel Test We examine the underlying logic of the testable hypothesis in terms of internal attribution tendency and the influence of political connection on CEO pay. Anticorruption and Internal Attribution Tendency We posit that boards might lean towards attributing fluctuations in corporate performance to internal efforts rather than external factors. Consequently, they exhibit a pronounced internal attribution tendency. According to Shi, Chen, and Li ( 2023 ), internal attribution tendency is measured as the ratio of the number of internal attribution words to the number of both internal and external attribution words. Meanwhile, a higher frequency of first-person pronouns in causal attribution statements within MD&A indicates a marked internal attribution tendency. The list of words pertaining to internal attribution includes: I, Id, I’d, I’ll, Im, I’m, Ive, I’ve, me, mine, my, myself, lets, let’s, our, ours, ourselves, us, we, we’d, we’ll, we’re, weve, we’ve. the list of words pertaining to external attribution includes environment, demand, supplier, supply, customer, client, consumer, government, regulation, economic, economy, market, competitor, rival, competition, weather, disaster, industry (Shi et al., 2023 ). We present the findings in column (1) of Table 11 . The coefficient of Firm exposure to anticorruption enforcement × Industry-adjusted ROA is positively and significant ( β = 0.006, p < 0.05), suggesting that the boards are more likely to internally attribute performance when corrupted official are ousted. Table 11 Channel Test Variables (1) (2) (3) Internal attribution tendency Industry-adjusted CEO pay Firm age 0.781 ** -0.109 *** -0.110 *** (0.256) (0.005) (0.005) Firm size -0.008 0.262 *** 0.262 *** (0.023) (0.014) (0.014) Leverage 0.063 -1.888 *** -1.886 *** (0.090) (0.080) (0.080) Board size 0.008 0.050 *** 0.049 *** (0.012) (0.010) (0.010) Board independence 0.485 -0.508 + -0.509 + (0.301) (0.307) (0.307) R&D -0.001 0.050 *** 0.051 *** (0.005) (0.004) (0.004) CEO duality -0.039 1.605 *** 1.605 *** (0.033) (0.035) (0.035) State monopoly -0.452 ** 2.127 *** 2.127 *** (0.159) (0.062) (0.062) Firm dependence government -0.126 0.769 *** 0.773 *** (0.162) (0.207) (0.207) Political contestability -0.027 -0.008 -0.008 (0.017) (0.023) (0.023) GDP 0.049 0.278 *** 0.278 *** (0.132) (0.020) (0.020) SOEs 0.077 -1.393 *** -1.392 *** (0.071) (0.036) (0.036) ETC 6.444 ** -7.771 ** -7.728 ** (2.268) (2.413) (2.413) Political embeddedness -0.014 -0.071 *** -0.072 *** (0.012) (0.014) (0.014) Firm exposure to anticorruption enforcement 0.025 -0.171 *** -0.135 ** (0.039) (0.038) (0.044) Industry-adjusted ROA -0.007 ** (0.002) Firm exposure to anticorruption enforcement × Industry-adjusted ROA 0.006 * (0.003) Political connection 0.166 *** 0.217 *** (0.031) (0.043) Firm exposure to anticorruption enforcement × Political connection -0.105 + (0.061) Industry/Year/Province FE YES YES YES Constant -2.117 -9.548 *** -9.554 *** (1.618) (0.353) (0.353) adj. R 2 0.276 0.234 0.234 F 3.028 503.837 472.566 Notes. N = 24,734. The table presents the results of mechanism test. Model 1 presents the results for internal attribution tendency. Internal attribution tendency is measured as the ratio of the number of internal attribution words to the number of both internal and external attribution words. Model 2 and 3 represents the results of political connection on CEO pay. Political connection equals to one if CEO or chairman was a number of People’s Congress or the Chinese People’s Political Consultative conference, or has been an officer of the central or regional government, zero otherwise. Standard errors are in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. ---Insert Table 11 about here--- The Influence of Political Connection on CEO Pay In addition, we assess the influence of political connection on CEO pay. We suggest that ousted corrupt officials after anticorruption shocks leads to a breakdown in the CEO's external relationships, which in turn makes the market more efficient. Therefore, political connections are associated with higher CEO pay, but this relationship is weakened after anticorruption shocks. According to Xiang et al. ( 2022 ), political connection equals one if the CEO or chairman was a number of People’s Congress or the Chinese People’s Political Consultative conference, or had served as an officer of the central or regional government, and zero otherwise. The results reported in columns (2) and (3) of Table 11 provide supporting evidence. We find that the effect of political connection on CEO pay is positive ( β = 0.166, p < 0.001), but the effect of Firm exposure to anticorruption enforcement × political connection on CEO pay is negative ( β = -0.105, p < 0.1). These results suggest that boards appear to shift their attributions about CEOs’ performance from external to internal factors after anticorruption shocks. DISCUSSION Theoretical Contributions First, our study extends research on the antecedents of CEO PPS, an important topic in CEO compensation research. Most previous research has adopted the agency perspective that CEO PPS is to resolve the principal-agency issue, and has overlooked the examination of PPS issues from the perspective of institutional environmental changes. Our study takes a novel approach by highlighting the role of ousted corrupt officials in influencing CEO PPS, we thus underscore the significance of considering broader institutional dynamics and their impact on executive compensation structures. By doing so, we contribute to a more comprehensive understanding of the intricate factors shaping CEO PPS. Second, we contribute to the literature on outcomes of public governance. Anticorruption is a type of public governance, and prior research has mostly examined its influence on macroeconomics, financial markets, corporate market strategies, and non-market strategies. Our research shifts the focus to examine its influence on CEO PPS, which serves as an important mechanism for efficient corporate governance. Third, our study extends the literature on political ties by differentiating between two distinct forms of firms’ political connections. We categorize these connections as ascribed business-government connections (pertaining to SOEs) and achieved business-government connections (linked to ETC), and find that these two types of political ties play different roles in moderating the effect of anticorruption on CEO PPS. As such, we contribute to the advancement of literature by theorizing on their unique contextual roles in influencing the relationship between anticorruption initiatives and CEO PPS. Practical implications This paper highlights key practical and policy implications from the study of anticorruption campaigns on corporate governance. It emphasizes that government-led anticorruption efforts significantly boost political institutions and reduce firms’ dependence on government, encouraging shifts towards internal pay-for-performance mechanisms. This underscores that anticorruption initiatives are effective tools for improving corporate governance and addressing principal-agency issues. With regard to managerial implications, environment changes can greatly affect the effects of corporate strategies (Tan & Tan, 2005 ). In light of pervasive political uncertainties stemming from scandals, national elections, political turnover, global summits, and institutional changes (Julio & Yook, 2012 ), corporate political strategy necessitates a comprehensive evaluation from a broader perspective. Managers of non-SOEs or firms characterized by high ETC, navigating through increasingly unpredictable political landscapes, must remain vigilant to potential risks associated with political shocks. Importantly, such shocks may result in more significant disruptions for non-SOEs compared to SOEs. CONCLUSION AND LIMITATIONS To investigate how firm exposure to anticorruption enforcement affects CEO PPS, this study employs ordinary least squares regression, using a sample of 3,215 Chinese A-share listed firms from 2012 to 2018. The empirical results show that (1) there is a positive association between firm exposure to anticorruption enforcement and CEO PPS, a conclusion that remains robust after robustness checks including parallel trend analysis, placebo tests, PSM-DID, alternative measures of dependent variables, and alternative meaure of anticorruption shock. (2) This positive association is weaker for SOEs, but stronger for firms with higher ETC and for firms located in provinces where the political embeddedness of ousted corrupt officials is stronger. (3) Moreover, the channel test reveals that boards appear to shift their attributions about CEOs’ performance from external to internal factors after anticorruption shocks. Our study also has several limitations, which point to potential directions for future research. First, this study dose not involve a direct measurement of board attributions. Our assertion is that boards formulate internal attributions concerning firm performance, influenced by anticorruption measures. The removal of corrupt officials prompts board members to form precise evaluations regarding the CEO’s responsibility for overall company performance. Nevertheless, firm performance is the outcomes of CEO’s ability and effort, but our data do not provide insights into whether boards base their decisions on CEO compensation by assessing the CEO’s ability or effort. Future research could implement the field studies and experiments through offering more detailed insights into board attributions. Second, despite exploiting the staggered timing of provincial-level removals, our design cannot fully rule out all forms of time-varying provincial shocks or other concurrent institutional changes that might influence both firm governance and pay practices. In addition, our primary exposure measure, a three-year post-event indicator for provincial removal, is an operational choice; sensitivity to alternative window lengths could be explored in future work. Third, the measures of political ties (e.g., abnormal ETC and official tenure) capture particular dimensions of business–government linkage but do not exhaustively cover all forms of political capital. Future studies could explore additional forms of political connections in the relationship between anticorruption and CEO PPS. Finally, our sample is limited to publicly listed Chinese firms; therefore, caution is warranted in generalizing to private firms or other national contexts. Future studies can test our findings in other institutional contexts. Declarations Data availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. Acknowledgments This paper is supported by the National Natural Science Foundation of China (Grant No. 72102003; 72091310; 72091314; 72102184; 72302068; 72402170); National Social Science Foundation of China (Grant No. 21&ZD137); Humanities and Social Science Project, Ministry of Education of China (Grant No. 23YJC630195; 21YJC630128); Shaanxi Social Science Foundation (Grant No. 2023R023); Starting grant from Peking University; Fundamental Research Funds for the Central Universities (Grant No. HIT.HSS.202301). Competing interests The author declares no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. References Anokhin, S., & Schulze, W. S. 2009. Entrepreneurship, innovation, and corruption. Journal of Business Venturing , 24(5): 465–476. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7477999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":536038161,"identity":"76e94e4a-abf8-4608-984f-74e8b302a74f","order_by":0,"name":"Hongfei Ruan","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongfei","middleName":"","lastName":"Ruan","suffix":""},{"id":536038163,"identity":"71f24601-78de-4bd2-a8e3-105298935018","order_by":1,"name":"Yi Xiang","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Xiang","suffix":""},{"id":536038165,"identity":"8578f18b-ab47-458d-87a6-d01101549c5c","order_by":2,"name":"Ying Zhang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":536038166,"identity":"03c6f42c-8c6f-46f6-818c-98a3465fc3f7","order_by":3,"name":"Li Tong","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Tong","suffix":""},{"id":536038167,"identity":"f2c3d7cf-4306-4cca-8bfa-d79533a2f8dd","order_by":4,"name":"Yongzhi Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDCCAxAqgY29B8xmbCBeC88ZhgMHSNLCIJED5hDWwncjx/Bxwa+6PD7JtwcPf2Cwkd1wgPnZA3xaJG/kGBvP7GMrZpPOSwA6LM14wwE2cwN8Wgxu5JhJ8/bwJLZJ5xgAtRxO3HCAh02CCC0SiW2SZ0Ba/hOpheeHQWKbBA9IywHCWiTPPCs25m1ISGzjATrsjEGy8czDbGZ4tfAdT974mOdPXeL89jPGHyoq7GT7jjc/w6uFgYHDgIGxDe5OIGbGrx4I2B8wMPwhqGoUjIJRMApGMgAAyo9PS6S/Xg8AAAAASUVORK5CYII=","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yongzhi","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-08-28 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10:10:10","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":334530,"visible":true,"origin":"","legend":"","description":"","filename":"c6a9a0cbb086424fa93ccfb7d9438d3f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/6c53344c074de2c463571930.xml"},{"id":94749977,"identity":"d00d0b13-9b6f-44c2-aae3-d441db1da3f9","added_by":"auto","created_at":"2025-10-30 10:10:10","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":332463,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/f026f391c5ee58b8b2e7aaac.html"},{"id":94749959,"identity":"8d6b99a3-6435-4665-9774-1ca2bc69b8e7","added_by":"auto","created_at":"2025-10-30 10:10:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction Effect of Firm Exposure to Anticorruption Enforcement (FEAE) and Industry-Adjusted ROA\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/35ce5ec01014279bbd8de075.png"},{"id":94749963,"identity":"3ef79b09-fb90-421e-8e07-c1c14eafa9ab","added_by":"auto","created_at":"2025-10-30 10:10:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating Effect of SOEs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/4fb936000366de29c4b93ac4.png"},{"id":94749960,"identity":"28ca4290-9f91-4768-b9d8-f968de0515d3","added_by":"auto","created_at":"2025-10-30 10:10:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating Effect of ETC\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/f9fa6096ac563370d3f15ee0.png"},{"id":94749967,"identity":"d3905420-6b19-4846-bda3-c1ea74f18d63","added_by":"auto","created_at":"2025-10-30 10:10:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":245054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating Effect of Political Embeddedness\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/64f6697da0a5e95dbc572378.png"},{"id":94824122,"identity":"31c9f3fa-5482-4651-a93b-412e09760663","added_by":"auto","created_at":"2025-10-31 06:48:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Estimated Coefficient of Placebo Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes. The X-axis is the estimated coefficient of Firm exposure to anticorruption enforcement generated randomly 500 times. The filled dot is the P-value of the estimated coefficient, and the solid line is the kernel density distribution of the estimated coefficient. The right vertical dotted line is the estimated coefficient (0.028) of the actual policy\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/342fe234f880db7fceb635c1.png"},{"id":94984855,"identity":"b2b7b846-a393-46b9-a07e-1e9ec5791a7d","added_by":"auto","created_at":"2025-11-03 06:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4632144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7477999/v1/b58a9649-71ba-4d1c-82c7-702c80cd44d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From external to internal: The influence of ousted corrupt officials on CEO pay-for-performance sensitivity","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eA primary role of board involves in determining CEO pay (Core, Holthausen, \u0026amp; Larcker, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Jensen \u0026amp; Murphy, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Laux \u0026amp; Laux, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Seo, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To decide CEO PPS is regarded as one of the most important considerations since it\u0026rsquo;s a pivotal component of CEO compensation design and reflects a critical mechanism for aligning the interests of executives and shareholders (Bebchuk \u0026amp; Fried, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chadwick, Guthrie, Xing, \u0026amp; Yan, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garen, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Van Essen, Otten, \u0026amp; Carberry, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The majority of studies to date almost exclusively focused on how internal agency-related factors shapes CEO PPS, such as level of strategic investments (Shi, Connelly, Mackey, \u0026amp; Gupta, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), CEO political promotion (Cao, Lemmon, Pan, Qian, \u0026amp; Tian, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Perry \u0026amp; Zenner, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and firm family-controlled status (Chen, Chittoor, \u0026amp; Vissa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, we know little about how external sociopolitical factors influence a firm\u0026rsquo;s policy of CEO PPS. Studying the impact of sociopolitical factors on CEO PPS is important because \"institutional pressures, ..., do not just \"enter\" an organization\u0026mdash;they are interpreted, given meaning and \"represented\" by occupants of structural positions\" (Greenwood, Raynard, Kodeih, Micelotta, \u0026amp; Lounsbury, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e: 342).\u003c/p\u003e\u003cp\u003eTo advance the research, our study leverages the context of anticorruption campaign, the landmark sociopolitical event in China (Ben, Li, Duncan, \u0026amp; Xu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cao, Wang, \u0026amp; Zhou, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This campaign results in significant changes in institutional environment and business practices. It led to a fundamental shift in the political landscape of China, impacting governance, power dynamics, and bureaucratic practices. The sheer number of officials investigated and disciplined underscores the campaign's extensive reach and its potential to reshape political behavior and norms (Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Griffin, Liu, \u0026amp; Shu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This campaign also influences business practices and corporate governance in China. The crackdown on corruption has altered the risk landscape for businesses, necessitating new strategies for compliance and corporate ethics (Griffin, Liu, \u0026amp; Shu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hauser, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sari, Cahaya, \u0026amp; Joseph, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe build on political perspective (Spicer, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and attribution theory (Heider, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Weiner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) to theorize that business logics under politically corrupted environment are characterized with resource acquisition based on \u003cem\u003eguanxi\u003c/em\u003e and nepotism (Fan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Huang \u0026amp; Rice, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Anticorruption campaign will lead to a thorough revision of the regulatory framework governing market activities and give rises to a predictable and transparent regulatory environment as well as increasing marketization (Bu, Hanspal, \u0026amp; Liao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cao, Wang, \u0026amp; Zhou, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We thus suggest that, in a corrupted environment, firms will tend to make external attribution about corporate failure or success, \u003cem\u003ei.e.\u003c/em\u003e, to attribute success or failure to government \u003cem\u003eguanxi\u003c/em\u003e. However, after ousted corrupt officials in a firm\u0026rsquo;s location, firms will be exposed to anticorruption enforcement. We suggest that firms are more likely to make internal attribution, i.e., attribute success or failure to their own business acumen. In this regard, firms will hold CEOs become more responsible for financial performance, and the manifestation of such internal attribution of responsibility is the increase of CEO PPS. Thus, we posit that the firm exposure to anticorruption enforcement will increase a firm's CEO PPS. We further explore how the influence of firm exposure to anticorruption enforcement on CEO PPS varies across different types of political involvement. Our analyses show that the positive relationship between firm exposure to anticorruption enforcement and CEO PPS will be weaken if a firm is a SOEs. We also find that, when the political embeddedness of ousted corrupt officials due to anticorruption campaigns is larger, the positive relationship between firm exposure to anticorruption enforcement and CEO PPS will be strengthened. We also suggest that, when a firm has a stronger reliance on entertainment and travel costs (ETC), it will be more responsive to the anticorruption campaign, and the positive effects of firm exposure to anticorruption enforcement on CEO PPS will be strengthened.\u003c/p\u003e\u003cp\u003eThis study makes several contributions to extant literature. First, in contrast to the prevalent principal-agent viewpoint that traditionally elucidates corporate practice of CEO PPS (e.g., Chen, Chittoor, Vissa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shi et al, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we introduce a crucial yet understudied theoretical angle derived from the significant and pivotal change in political institutions in China to unravel how firms respond to the Chinese anticorruption campaign by altering the CEO\u0026rsquo;s PPS, thereby enriching the existing literature on the antecedents of CEO compensation design. Second, our contribution extends to the public governance literature by broadening the impact of significant form of public governance (e.g., ousted corrupt officials) from the previous focus on macroeconomics, financial markets, and corporate strategies (e.g., Chen, Lu, Heng, \u0026amp;Tan, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to an efficient incentive mechanism in corporate governance (e.g., CEO PPS). Third, our study advances the business-government connections literature by theorizing how the distinctive contextual roles played by both sides of business-government connections\u0026mdash;firms and officials\u0026mdash;affect the relationship between firm exposure to anticorruption enforcement and CEO PPS. On one hand, ascribed business-government connections (e.g., SOEs) attenuate the aforementioned relationship, while achieved business-government connections (e.g., firms with higher ETC) strengthen it. On the other hand, ousted corrupt officials with larger local embeddedness amplify the strength of the above relationship.\u003c/p\u003e\n\u003ch3\u003eINSTITUTIONAL BACKGROUND AND HYPOTHESES\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCEO PPS\u003c/h2\u003e\u003cp\u003eBased on the agency theory, there is a conflict of interest between the owners (principals) and the managers (agents), and the agents may attempt to maximize their interests at the expense of the principals. One proposed solution to the agency problem is to align CEO compensation based on the firm performance (Brick, Palmon, \u0026amp; Wald, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Murphy, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Hoi, Wu, \u0026amp; Zhang, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, a body of research has identified that boards will reward CEOs for good performance and punish them for poor performance (Boschen, Duru, Gordon, \u0026amp; Smith, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In fact, external stakeholders such as shareholders and analysts often criticize boards when CEO pay becomes separated from firm performance (Bebchuk \u0026amp; Fried, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe CEO PPS is typically applied in the following scenarios. First, from the perspective of CEO characteristics, as an internal corporate governance mechanism, CEO compensation is one of the important ways to overcome the CEO\u0026rsquo;s risk aversion (Dittmann, Maug, \u0026amp; Spalt, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wruck \u0026amp; Wu, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, since Republican-leaning CEOs tend to be more conservative (Arikan, Kara, Masli, \u0026amp; Xi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Unsal, Hassan, \u0026amp; Zirek, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), boards thus offer more performance-based pay to these individuals. Second, when a company is in crisis or undergoing significant changes, CEOs may be granted higher compensation to facilitate major strategic transformations. Furthermore, the attitudes of board members play a crucial role in determining whether CEO pay for performance mechanisms are adopted, and this is closely tied to the decisions made by the CEO. For instance, when CEOs invest in high levels of growth, it provides the board with a target for their internal attributions, intensifying the PPS (Shi, Connelly, Mackey, \u0026amp; Gupta, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnticorruption Campaign in China\u003c/h3\u003e\n\u003cp\u003eIn emerging markets such as China, the government controls and allocates resources that are critical to the survival and development of firms (Fengyan, Hongjuan, Tan \u0026amp; Qi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, rent-seeking government officials extract resources from firms through the imposition of concealed costs (Jung \u0026amp; Lee, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, government possess the authority to either bestow preferential treatment upon businesses or levy additional fees and fines upon them, which cause government officials' rent-seeking incentive (Chen, Li, Su, \u0026amp; Sun, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gao, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e: 176).\u003c/p\u003e\u003cp\u003eTo address the increasing corruption, China has initiated an unparalleled, for-reaching, extensive, and lasting anticorruption campaign President Xi Jinping since the 18th National Congress of the Communist Party (Branigan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Giannetti, Liao, \u0026amp; Yu, 2021). Shortly after assuming leadership in China on November 15, 2012, Xi promptly promulgated the Eight-Point Regulation within the initial month of his tenure, manifesting a resolute commitment to combat corruption by addressing both high-ranking officials (\"tigers\") and lower-level functionaries (\"flies\") (Griffin, Liu \u0026amp; Shu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Over the preceding decade, the Central Commission for Discipline Inspection (CCDI), tasked with spearheading the anticorruption campaign and conducting inquiries into officials, has undertaken investigations involving nearly 5\u0026nbsp;million officials and party members across various echelons.\u003c/p\u003e\u003cp\u003eWith the notable successes in the anticorruption endeavors, the potential ramifications of these efforts on China's sociopolitical environment have garnered escalating scrutiny, and scholars have undertaken a substantial volume of research to examine the implications of anticorruption on the economy and financial markets (Griffin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For its impact on a firm's value, some studies found that the campaign decreases the value of political connections with private firms (e.g., Fengyan et al, 2021; Liu \u0026amp; Ying, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but helps improve firm performance. In addition, it can influence firms' market strategies such as declining investment expenditure (Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and firms' non-market strategies such as reducing entertainment expenditures (Griffin, Liu \u0026amp; Shu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suppressing negative information release (Cao, Wang \u0026amp; Zhou, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eExposure to Anticorruption Enforcement and PPS\u003c/h3\u003e\n\u003cp\u003eIn China, the government controls vital resources key to firm survival and growth (Li, Meng, Wang, \u0026amp; Zhou, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Marquis \u0026amp; Qian, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Firms\u0026rsquo; interaction with the government hinge more on external factors like political connections and bribery-based exchanges to secure business advantages and regulatory benefits (Cull, Li, Sun, \u0026amp; Xu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Millington et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In environments marked by corruption, business operations predominantly rely on external attributions, where corporate managers often lack the autonomy to make independent decisions. The famous assertion by Lord Acton that 'power tends to corrupt, and absolute power corrupts absolutely,' underscores the overwhelming impact of corruption, which often restricts managerial discretion within the business environment. Firms are compelled to devote substantial resources to sustaining business-government relations, positioning these connections as central to business strategy rather than market-driven considerations (Iriyama, Kishore, \u0026amp; Talukdar, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, business decisions often depend on external approvals and interactions, especially with government officials (e.g., Huang \u0026amp; Rice, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It results in that the boards believe the key to business success is to win access to resources controlled by the government. This leads to an external attribution for business success and failure.\u003c/p\u003e\u003cp\u003eHowever, after the ousted corrupt officials, firms are less subjective to government intervention and less depend on the resources controlled by government (Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Griffin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This shift generally leads to a reduced emphasis on leveraging government resources and an increased focus on enhancing internal management practices and fostering innovation (Anokhin \u0026amp; Schulze, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Iriyama, Kishore, \u0026amp; Talukdar, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xu \u0026amp; Yano, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Anticorruption strategies necessitate that managers engage in more strategic thinking and planning (Bahoo, Alon, \u0026amp; Paltrinieri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They will obtain more discretion over business operations (Owusu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, after firm exposure to anticorruption enforcement, firms may rely more on internal resource and capability building and thus may make more internal attribution for financial outcomes. Firms thus may pivot their focus toward efficiency logic (Kong et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou, Gao, \u0026amp; Zhao, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the board may have strong motivation to attach a CEO\u0026rsquo;s effort to performance (Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As such, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eExposure to anticorruption enforcement in a firm\u0026rsquo;s location is positively associated with the firm's CEO PPS.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eModerating Role of SOEs\u003c/h3\u003e\n\u003cp\u003eThe distinction between SOEs and private firms is quite pronounced, primarily rooted in their differing objectives, management structures, and innovation pursuits. SOEs serve both economic and political goals such as preserving employment, ensuring social stability, fulfilling national strategic goals (Du, Tang, \u0026amp; Young, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang \u0026amp; Luo, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, non-SOEs are generally profit-oriented, striving to increase shareholder value (Liu \u0026amp; Tian, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Renneboog, Simons, \u0026amp; Wright, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Studies also show that SOEs are typically more bureaucratic with operational decisions often influenced by government political cycles, and non-SOEs often have more streamlined management structures characterized by flexibility and market responsiveness (Chen, Sun, Tang, \u0026amp; Wu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our context of firm exposure to anticorruption enforcement, we expect that SOEs are less affected by ousted corrupt officials than non-SOEs, and thus propose that the positive relationship between firm exposure to anticorruption enforcement and CEO PPS is less pronounced for SOEs. First, compared with SOEs, non-SOEs are more subject and sensitive to the political intervention. The rent-seeking theory (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) posits that private firms have stronger motivation to seek benefits from the political system through various means including lobbying. Second, private firms face serious industry entry restriction, higher capital and energy costs, and greater difficulty in obtaining approval for business projects (Gao \u0026amp; Yang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such institutional constraints make harder for them to achieve a good performance or even make a survival. Compared with SOEs, non-SOEs may be more dependent on resources and support offered by politicians (Cao, Pan, Qian, \u0026amp; Tian, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu, Luo, \u0026amp; Tian, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, ousted corrupt officials will result in a larger disruption for non-SOEs than SOEs (Kong et al., 2017; Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and non-SOEs\u0026rsquo; responses will be more acute. We, therefore, suggest the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe positive relationship between exposure to anticorruption enforcement in a firm\u0026rsquo;s location and a firm\u0026rsquo;s CEO PPS will be weaker for SOEs.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eModerating Role of ETC\u003c/h3\u003e\n\u003cp\u003eTo secure favorable treatment and gain a competitive edge, managers are highly motivated to foster close ties with the government (Cai et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Recent research has highlighted the significant role of ETC as a strategic investment by firms in nurturing these relationships, which typically manifests as hosting and entertaining politicians, aiming to secure protection and support from the government (Cai et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We expect that firms with higher ETC will be more motivated to adapt their attribution to CEO PPS in accordance with the power shifts and staff turnover caused by ousted corrupt officials.\u003c/p\u003e\u003cp\u003eFirms often engage in relationship-building activities, including entertainment and travel with government officials, to secure favorable policies and business opportunities (Li, Meng, Wang, \u0026amp; Zhou, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, such practices have been directly targeted by recent anticorruption efforts (Zhang, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This shift challenges the established mechanisms firms use to navigate the business-government interface in China, requiring a strategic reorientation. We suggest that such strategic reorientation occurs with a shift from government-centric to market-centric and firms with higher ETC will feel more urgent to focus on the internal development as well as internal attribution of firm financial performance (Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, after the corrupt officials are ousted in the aftermath of anticorruption shocks, other sitting officials commonly strive to cleanse the influence of their corrupt peers in public and private sectors in order to show their determination to fight corruption (Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Simultaneously, successor officials, aiming to minimize the negative legacies of their predecessors, tend to marginalize firms associated with the ousted officials (Pan \u0026amp; Tian, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, firms relying more on ETC before anticorruption may feel more pressure to attract talented leaders, thereby setting a higher PPS (Falato, Li, \u0026amp; Milbourn, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We thus expect that:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe positive relationship between exposure to anticorruption enforcement in a firm\u0026rsquo;s location and a firm's CEO PPS will be stronger for a firm with larger ETC.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModerating Role of The Embeddedness of Ousted Corrupt Officials\u003c/h2\u003e\u003cp\u003eWithin the Chinese state bureaucracy, social relations have long been essential, not only for carrying out one's assigned tasks but also for advancing one's career (Zhou, Ai, \u0026amp; Lian, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The political embeddedness of government officials contributes to connections between officials and local enterprises, and lead to an increase in corporate rent-seeking behavior (Murphy, Shleifer, \u0026amp; Vishny, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sun, Mellahi, \u0026amp; Thun, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In our context of ousted corruption officials, we suggest that the influence of firm exposure to anticorruption enforcement on CEO PPS may vary with political embeddedness of ousted corrupt officials. If the ousted corrupt official had a longer tenure in the province, it indicates there will be a more severe redistribution of political powers. Firms located in this area may face more challenges in reorienting their strategies and business operations. Firms will feel more pressure to cut off the connections with the local political system and it's also more challenging form them to rebuild the relationship with the incoming political officials (Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Especially, the anticorruption invokes the strong monitoring on the government-business relationships (Griffin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To respond to these pressures, firms and their boards will be more responsive to the occurrence of outdated corrupt officials with longer tenure and are eager to shift their attention to merit-based criteria and corporate internal development to a larger extent, thereby increasing the sensitivity of CEO pay and corporate performance. We thus expect that:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe positive relationship between exposure to anticorruption enforcement in a firm\u0026rsquo;s location and a firm's CEO PPS will be stronger when ousted officials have larger local embeddedness.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"METHOD","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData and Sample\u003c/h2\u003e\u003cp\u003eChina is an appropriate empirical context for this study because of pervasive national anticorruption campaigns that Chinese president Xi Jinping launched the Campaign at the 18th People\u0026rsquo;s National Congress in November 2012. After anticorruption campaign, 110 top provincial officials (province level and deputy-province level) across 31 provinces and removed them from their posts between 2012 and 2018. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes name of officials removed, the year, and province based on data from the website of CCDI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ccdi.gov.cn/scdc\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRemoval of Top Provincial Officials on Corruption Charges During the Anticorruption Campaign\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName of Official\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eName of Official\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eName of Official\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBo Xilai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2012.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSui Fengfu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2014.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeilongjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHuang Xingguo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2016.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLi Chuncheng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2012.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSichuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHan Xuejian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2014.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeilongjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChen Shulong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2016.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnhui\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNi Fake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnhui\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYang Weize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJiangsu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWu Tianjun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2016.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHenan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang Suyi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInner Mongolia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLu Wucheng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGansu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZhang Wenxiong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2016.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHunan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuo Yongxiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSichuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXu Aimin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYu Haiyan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGansu\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLi Daqiu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi Xinliang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZhejiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLi Wenke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiao SHaohua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuizhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLi Zhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXinjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChen Xu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShanghai\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen Baihuai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHubei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXu Gang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFujian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYang Chongyong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuo Youming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHubei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYan Shiyuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShandong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZhou Chunyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnhui\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLi Chongxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSichuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHan Zhiran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInner Mongolia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWang Hongjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen Anzhong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLe Dake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXizang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZeng Zhiquan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGuangdong\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTong Mingqian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHunan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLiu Zhiyong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWei Minzhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShaanxi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJin Daoming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZhou Benshun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAi Wenli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJi Wenlin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHainan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGu Chunli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJilin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHe Ting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZu Zuoli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSun Qingyun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShaanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eXu Qianfei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJiangsu\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShen Peiping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYunnan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSu Shulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFujian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWang Sanyun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGansu\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYao Mugen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAi Baojun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShanghai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSun Zhengcai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJing Chunhua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLv Xiwen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBeijing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMu Huaping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMao Xiaobing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQinghai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBai Xueshan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNingxia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLiu Qiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2017.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYang Baohua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHunan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCao Jianfang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYunnan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJi Xiangqi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShandong\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTan Xiwei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWi Hong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSichuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLi Yihuang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDu Shanxue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLiu Lizu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFeng Xinzhu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShaanxi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLing Zhengce\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGai Ruyin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2015.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeilongjiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLiu Jun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGuangxi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhao Zhiyong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJiangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLiu Zhigeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuangdong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBai Xiangqun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eInner Mongolia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQiu He\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYunnan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLu Ziyue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZhejiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWang Xiaoguang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGuizhou\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWu Changshun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHe Jiatie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHubei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePu Bo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGuizhou\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan Xiancong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnhui\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZhang Lifu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHainan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJin YUhui\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJilin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTan Li\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHainan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWang Yang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLi JInxiu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJilin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen Tiexin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLi Chengyun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSichuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWang Erzhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJilin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen Chuanping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZhang Yue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChen Zhifeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBai Yun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKong Lingzhong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuizhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWang Tie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHenan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNie Chunyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSu Hongzhang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLi Shixiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBeijing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRen Runhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShanxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYang Zhenchao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnhui\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJin Suidong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHenan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOan Yiyang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInner Mongolia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLi Yunfeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJiangsu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eXin Yun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eInner Mongolia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQin Yuhai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHenan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLai Derong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuangxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eQian Yinan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShaanxi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhu Mingguo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuangdong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYin Hailin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMiao Ruilin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2018.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eJiangsu\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiang Bin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHebei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZheng Yuzhuo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLiaoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNotes.\u003c/em\u003e This table summarizes the removal of top provincial officials on corruption charges after the inception of the anticorruption campaign. Included are removals of province level and deputy-province level officials. The data are compiled from the website of the Central Commission for Discipline Inspection.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003eWe exacted information from multiple data sources to construct our dataset. First, we collected the resumes of all ousted officials at deputy-province level and province level from the CCDI website, including the official\u0026rsquo;s birth year, positions and ranking of indicted corrupt officials, and the specific time of their removal. Second, the sample we use in this study is all listed firms in Shanghai Stock Exchange and Shenzhen Stock Exchange. Firm-level information comes from the China Stock Market and Accounting Research (CSMAR) database and the WIND database. Based on the described procedure and after merging with the other databases, our final main sample consists of 24,734 firm-year observations for 3,215 unique firms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMeasurements\u003c/h2\u003e\u003cp\u003e\u003cb\u003eDependent variable (Industry-adjusted CEO pay).\u003c/b\u003e According to Shi, Connelly, Mackey, and Gupta (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the dependent variable in our study is total CEO pay, which consists of salary, bonuses, the value of restricted stock granted, the value of options granted, long-term incentive payouts, and other compensation, which equals the natural logarithm of CEO total pay to fit a log-linear model (Banker, Darrough, Huang, \u0026amp; Plehn-Dujowich, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). We adjust CEO pay by the average of compensation levels of other CEOs in the same industry (based on three-digit SIC codes) to partial out the differences in CEO compensation across industries (Shi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent variable (industry-adjusted ROA).\u003c/b\u003e For our independent variable, firm performance, we use \u003cem\u003eindustry-adjusted return on assets (ROA)\u003c/em\u003e, which equals the difference between a focal firm's ROA and the average of ROA of other firms in the same three-digit industry (Chen, Chittoor, \u0026amp; Vissa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Particularly, stock options are a rarely used element of CEO compensation in Chinese stock markets, we do not use share price performance or change in shareholder wealth to measure firm performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModerators.\u003c/b\u003e Our primary moderator is \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e. This variable is the removal of a top provincial official on corruption charges, which equaled one for the three-year period after the event and zero otherwise (Fang, Lerner, Wu, \u0026amp; Zhang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also consider three additional moderators that can affect the role of firm exposure to anticorruption enforcement in shaping the PPS. The first is \u003cem\u003eSOEs\u003c/em\u003e, which is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders (Marquis \u0026amp; Qian, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang, Wong, \u0026amp; Xia, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe second additional moderator is \u003cem\u003eETC\u003c/em\u003e. According to Fang et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we first estimate a regression model in which the dependent variable is the entertainment expenses divided by sales revenues. The predictor variables in the model include total sales, total assets, and the log of per capita GDP of the firm\u0026rsquo;s home province. We then use the residual from this regression model as a proxy for abnormal entertainment expenses, which reflects a firm\u0026rsquo;s relationship expenditure with government.\u003c/p\u003e\u003cp\u003eThe third moderator is \u003cem\u003epolitical embeddedness of ousted corrupt officials\u003c/em\u003e, which equals to the time that corrupt officials have served in provincial government departments in this province (e.g., Lester, Hillman, Zardkoohi, \u0026amp; Cannella, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eControl variables.\u003c/b\u003e We control for several variables that are expected to have an impact on CEO compensation design, including \u003cem\u003efirm size, firm age, leverage, board size, board independence, R\u0026amp;D, CEO duality, state monopoly, firm dependence government, political contestability\u003c/em\u003e, and \u003cem\u003eGDP\u003c/em\u003e (e.g., Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang, Marquis, \u0026amp; Qiao, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We controlled for province-, industry- and year-fixed effects in the regressions to account for unobserved systematic heterogeneities across different firms and years, respectively. We present detailed definitions in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definitions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinitions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe natural log of the number of years since the firm was founded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe natural log of total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe ratio of the total debt to total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe natural log of number of board directors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe percentage independent directors on the boards\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe total R\u0026amp;D expenditure scaled by revenue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquals to one if the CEO is also the chairman of the firm, and zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe proportion of SOEs\u0026rsquo; sales to those of the total industry (at three-digit level) for each year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe ratio of the government subsidies divided by the firm\u0026rsquo;s annual operating income\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe ousted corrupt official\u0026rsquo;s prior political appointments, calculating the total number of different provinces in which they had served as province level and deputy-province level official prior to their current positions in the focal province\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP of the province where the focal firms are headquartered\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOEs is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe residual of the ETC divided by sales revenues\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe time that corrupt officials have served in provincial government departments in this province\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe removal of a top provincial official on corruption charges, which equaled one for the three-year period after the event and zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe difference between a focal firm's ROA and the average of ROA of other firms in the same three-digit industry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted CEO pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe difference between a focal firm's CEO pay and the average of CEO pay of other firms in the same three-digit industry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMain Effects\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the summary statistics for our sample, which consists of annual firm-level observations. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of Pearson correlation tests among the main variables in our sample. Firm-level industry-adjusted ROA was highly correlated with industry-adjusted CEO pay. To check the potential multicollinearity in our analyses, we calculated variance inflation factors (VIFs) for all our estimation models. Results show that the average VIF value was 1.46 (the maximum VIF was 2.31), indicating no significant concern about multicollinearity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary Statistics for All Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP25\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP75\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-27.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted CEO pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNotes.\u003c/em\u003e The table reports summary statistics of the main variables in the listed family-firm sample for the period of 2009\u0026ndash;2018. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. All financial amounts are measured in millions of RMB. All financial ratios are calculated annually using data from firms\u0026rsquo; annual statements.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations between All Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.170***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eleverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.170***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.161***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.453***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.090***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.230***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.361***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.147***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.083***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.174***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.162***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.171***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.092***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.186***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.101***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.229***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.192***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.237***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.296***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.162***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.044***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.074***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.130***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.096***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.026***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.017***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.016**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.018***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.013**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.038***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.024***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.181***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.137***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.133***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.204***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.142***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.249***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.348***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.314***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.276***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.295***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.297***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.382***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.065***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOfficial tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.212***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.084***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.131***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.044***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.150***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.303***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.060***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.100***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.141***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.058***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.203***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.078***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.050***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.263***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.044***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry-adjusted CEO pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.170***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.045***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.218***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.034***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.170***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.333***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.049***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.066***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.127***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.093***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.055***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.255***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.096***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.011*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.050***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOfficial tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.293***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.091***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.069***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.324***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.113***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.613***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.101***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.025***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry-adjusted CEO pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.144***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.293***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.014**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.261***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNotes. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. This table presents the Pearson correlation matrix among the main variables in our analysis. *, **, ***Statistical significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the regression results for the removal of corrupt officials. Model 1 focuses on the key independent variables: \u003cem\u003eIndustry-adjusted ROA\u003c/em\u003e and \u003cem\u003eFirm exposure to anticorruption enforcement\u003c/em\u003e. The main effect of \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e is positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), as expected, because CEOs receive higher compensation when the firm's ROA is performing well. Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e predicts that after anticorruption shocks, firms located in province with ousted corrupt officials will be motivated to increase CEO PPS. In Model 2, we add the interaction term between \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e and \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e. The result shows a positive and statistically significant coefficient (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) for \u003cem\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e, in the absence of any controls. Model 3 of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the full model controlling for time-varying firm characteristics. Model 4 adds province fixed effects, and Model 5 includes year and firm fixed effects. We continue to find positive and significant coefficients on the interaction term between \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e and \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e across these three models, thereby supporting Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Models: Firm Exposure to Anticorruption Enforcement and CEO Pay-for-Performance Sensitivity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.203\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.262)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.335\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.200\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.827\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.549\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.308)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.553\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.524\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.741\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.753\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.162)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.561\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.284\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.281\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.511\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.266\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.135)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.336\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.318\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.541\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.621\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.179\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.331)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.337)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.324)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.113\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.111\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.235\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.495\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.410)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.657)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e905.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e612.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e576.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e496.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. This table reports results of panel regressions of PPS before and after the removal of top provincial officials on corruption charges. The dependent variable is Industry-adjusted CEO pay, measured as the difference between a focal firm's CEO pay and the average of CEO pay of other firms in the same three-digit industry. Detailed variable definitions are found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In columns (1) and (2), only controls for industry and year dummies are included. Columns (3)\u0026ndash;(5) present the results that include control variables. Standard errors are in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003eFurthermore, we calculate the magnitude of the interaction effect when \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e and \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e take different values. We define high (low) values of \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e as the mean plus (minus) one standard deviation, and calculate the marginal effects of \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e on \u003cem\u003eindustry-adjusted CEO pay\u003c/em\u003e when \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e takes different values. We present these results in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. When \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e is zero (with all other variables set to their mean values), \u003cem\u003eindustry-adjusted CEO pay\u003c/em\u003e increases by 92.89% when \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e increases from its low to high value. When \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e is one, \u003cem\u003eindustry-adjusted CEO pay\u003c/em\u003e increases by 109.42% for the same increase in \u003cem\u003eindustry-adjusted ROA.\u003c/em\u003e Therefore, PPS is greater after a corrupt official\u0026rsquo;s downfall, indicating that board rewards CEO more for strong firm performance and penalize them more for poor firm performance after anticorruption shocks.\u003c/p\u003e\u003cp\u003e---Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eModerating Effects\u003c/h2\u003e\u003cp\u003eOur moderating hypotheses (Hypotheses 2, 3, and 4) predict that the impact of firm exposure to anticorruption enforcement on PPS is moderated by SOEs, ETC, and Political embeddedness. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the results for these three hypotheses. Models 1\u0026ndash;3 report the three-way interactions between \u003cem\u003eindustry-adjusted ROA\u003c/em\u003e, \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e, and the three moderators respectively. Model 4 is the full model, including all non-interacted effects and interaction terms.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerating Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.980\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.978\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.161\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.161\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.205\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.148\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.549\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.552\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.549\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.556\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.297)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.517\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.523\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.525\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.517\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.822\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.822\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.571\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.547\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.564\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.027)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.514\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.505\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.513\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.511\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.208)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.220\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.106\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.603\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.212\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.353\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.337)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.336)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.109)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.169)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times; Firm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.242\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.247\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.017\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times; Firm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.740)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.897\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.037\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.341)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.122\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.044\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.511)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness \u0026times; Firm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Year/Province FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.515\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.180\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.249\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.496\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.186)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e427.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e422.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e422.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e329.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. This table reports results of moderating analysis. SOEs is coded as one if the firm was owned by the Chinese government and its agencies and zero if ultimate owner is private shareholders. ETC is the residual of abnormal entertainment expenses, which reflects a firm\u0026rsquo;s relationship expenditure with government. Political embeddedness equals to the time that corrupt officials have served in provincial government departments in this province. Standard errors in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003epredicts that SOEs further moderate the interaction described in Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, such that the relationship is weaker for SOEs. Model 1 of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the coefficient estimate of \u003cem\u003eSOEs \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e is negative (\u003cem\u003eβ\u003c/em\u003e = \u0026minus; 0.019, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), thus supporting Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To gain a better understanding of the moderating effect of SOEs, we illustrate the three-way interaction in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We observe that the slope of the solid line is steeper on the left side of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e than on right side (firm exposure to anticorruption enforcement equals one), while the slope of the dotted line is flatter on the left than on the right (firm exposure to anticorruption enforcement equals zero), indicating that the role of firm exposure to anticorruption enforcement in shaping PPS is stronger in the presence of non-SOEs.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e---Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eproposes that ETC also moderate the interaction described in Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, such that the relationship is stronger when ETC is high. Model 2 of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the coefficient estimate of \u003cem\u003eETC \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e is positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.122, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), thus supporting Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further illustrates a steeper effect under high ETC conditions.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e---Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here---\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003eproposes that political embeddedness also moderate the interaction described in Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, such that the relationship is stronger when political embeddedness is high. Model 3 of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the coefficient estimate of \u003cem\u003ePolitical embeddedness \u0026times; Firm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e is positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), thus supporting Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further demonstrates a stronger effect under conditions of high political embeddedness.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e---Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRobustness Tests and Supplementary Analyses\u003c/h2\u003e\u003cp\u003eWe conducted a series of additional analyses to further assess the robustness of the above findings. These include parallel trend analysis, placebo tests, propensity-score-match, alternative measures of dependent variables, and alternative measures of anticorruption shocks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eParallel Trend Analysis\u003c/h2\u003e\u003cp\u003eWe perform a parallel trend test to analyze the response of PPS around the timing of ousted corrupt officials during anticorruption shocks. Our method assumes no prior differences in PPS between affected and unaffected firms, aside from anticorruption actions. Therefore, we introduce dummy variables to indicate the periods of ousted corrupt officials during anticorruption shocks.\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ek (k=-3, -2, -1, 0, 1, 2, 3)\u003c/em\u003e denotes the pre-period and after-period of \u003cem\u003efirm exposure to anticorruption enforcement\u003c/em\u003e. These seven dummy variables capture the dynamic effects of firm exposure to anticorruption enforcement on PPS. \u003cem\u003eControls\u003c/em\u003e denote the set of basic variables.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the coefficient of \u003cem\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e (for k\u0026thinsp;=\u0026thinsp;0, 1, 2, 3) are positively significant, whereas those of before firm exposure to anticorruption enforcement (for k=-1, -2, -3) are insignificant. These estimates demonstrate that anticorruption shocks have deferred positive effects on PPS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParallel Trend Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore [-3] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore [-2] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore [-1] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter [+\u0026thinsp;1] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter [+\u0026thinsp;2] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter [+\u0026thinsp;3] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; Before [-3] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; Before [-2] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; Before [-1] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; Current FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; After [+\u0026thinsp;1] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; After [+\u0026thinsp;2] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.021\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA \u0026times; After [+\u0026thinsp;3] FEAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Year/Province FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.440\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNotes. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. The table considers PPS for three years pre- and post- firm exposure to anticorruption enforcement (FEAE). Standard errors in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePlacebo Tests\u003c/h2\u003e\u003cp\u003eWhile we observe a positive impact of removing corrupt officials on PPS, this effect could be coincidental due to potential selection biases. To address this concern, we employ placebo tests within China's anticorruption campaign. By simulating firm exposure to anticorruption enforcement under counterfactual conditions, we evaluate if positive effects persist, indicating influence from unobservable systemic factors rather than anticorruption actions alone. We enhance the estimation power through conducting 500 simulated placebo tests involving random timings and regions of official downfalls.\u003c/p\u003e\u003cp\u003eWe graph the p-values and regression coefficients of 500 fictitious regressions in a probability density plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), revealing that the coefficients cluster around zero and approximate a normal distribution. This suggests that random factors do not significantly affect our results. Additional analysis shows that only 11 out of 500 spurious coefficients are statistically significant compared to the benchmark, confirming the robustness of our findings with a 97.8% certainty. These placebo tests underscore that PPS's response to anticorruption shocks is not due to chance, reinforcing the significant impact of firm exposure to anticorruption enforcement.\u003c/p\u003e\u003cp\u003e---Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eUsing Propensity Score to Identify Comparable Firms\u003c/h2\u003e\u003cp\u003eUsing a multiple regression model, we find that the removal of officials influences PPS, but covariate variables could weaken exchangeability between CEOs of affected and unaffected firms, thereby introducing estimation bias. To overcome this issue in our non-randomized setting, we apply the propensity-score matching (PSM) method to control for confounders and better estimate the effects of firm exposure to anticorruption enforcement. We match firms based on similar propensity scores derived from a probit regression model, which considers firm and regional characteristics before the anticorruption campaign. The pairs are chosen from provinces where officials were removed (Beijing, Shanghai, Ningxia) in November 2015 and matched with firms from other provinces for comparison. This procedure was conducted annually from 2009 to 2014. The second stage involves comparing matched firms annually, ensuring each affected firm was paired with an unaffected one with a similar propensity score. The result presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows a significantly positive coefficient on the interaction term \u003cem\u003ePost \u0026times; Treated \u0026times; Industry-adjusted ROA\u003c/em\u003e (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) which confirms our baseline finding and indicates a reliable impact of anticorruption enforcement on PPS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePSM-DID\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.798\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.386)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.241\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.347\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.142)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.040\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.414)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.319\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.262)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.428\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.230)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.519\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.197)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.104)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.709\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.936)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.185\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.348)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost \u0026times; Treated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.182\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost \u0026times; Treated \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Year/Province FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.545)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13,450. This table presents examine the impacts of firm exposure to anticorruption enforcement on PPS through PSM method. The dependent variable is Industry-adjusted CEO pay. The treated groups are firms whose headquarters are located in provinces for firm exposure to anticorruption enforcement before the year 2015, and the control groups are firms whose headquarters are located in Beijing, Shanghai, and Ningxia provinces. Standard errors in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eAlternative Measures of Dependent Variables\u003c/h2\u003e\u003cp\u003eOur baseline results focus on CEO pay. For robustness tests, we alternatively use chairman pay and the combined total pay of the CEO and chairman. For chairman pay, we adjust it by the average of compensation levels of other chairman in the same industry (based on three-digit SIC codes) to partial out the differences in chairman compensation across industries (\u003cem\u003eIndustry-adjusted chairman pay\u003c/em\u003e). Meanwhile, \u003cem\u003eIndustry-adjusted total pay\u003c/em\u003e equals the difference between total compensation and average of compensation levels of other chairman and CEO in the same industry (based on three-digit SIC codes). The results presented in columns 1\u0026ndash;2 of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e show that the coefficients on the interaction term \u003cem\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e are both significantly positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in column (1), \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in column (2)), which are highly consistent with the main resluts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAlternative Dependent Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry-adjusted Chairman pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustry-adjusted total pay\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.632\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.480\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.221\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.678\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.470\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.991\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.585\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.429)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.322)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.087\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.333\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.569\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.037)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.831\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.154\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.716\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.219)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.725\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.788\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.224)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.500\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.328\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.987\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.825\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.340)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.535)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.064\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Year/Province FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.622\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.351)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e577.352\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes.\u003c/em\u003e The table represents the results for chairman pay and total pay between chairman and CEO. Model 1 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19,568) show the result for industry-adjusted chairman pay, which is calculated by the difference between chairman compensation and average of compensation levels of other chairman in the same industry. Model 2 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734) show the result for industry-adjusted total pay, which is calculated by the difference between total compensation and average of compensation levels of other chairman and CEO in the same industry. Standard errors in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDeterrence Effect of Anticorruption Shocks\u003c/h2\u003e\u003cp\u003eWe hypothesize that the removal of top provincial officials on corruption charges prompts strategic changes in firms, but the anticorruption campaign itself might also have a deterrent effect before actual enforcement actions (Fang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We designate 2012 as the key year of the anticorruption shock and analyze subsidy decisions from three years before (2009\u0026ndash;2011) to three years after (2013\u0026ndash;2015). The variable \"Post_2012\" is set to one for the years 2013 to 2015 and zero for 2009 to 2011. Although this approach doesn\u0026rsquo;t perfectly isolate the shock effect, given potential concurrent events in China. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the results of this robust check, in column (2), the coefficeint on the interaction term \u003cem\u003ePost_2012 \u0026times; Industry-adjusted ROA\u003c/em\u003e is significantly positive ( \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the anticorruption campaign's threat significantly influence CEO PPS. In column (3), the coefficeint on the interaction term \u003cem\u003eSOEs \u0026times;Post_2012 \u0026times; Industry-adjusted ROA\u003c/em\u003e is significantly negative ( \u003cem\u003eβ\u003c/em\u003e = -0.024, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), further comfirming the moderating effect of SOEs, and the significantly positive coefficient on the interaction term \u003cem\u003eETC \u0026times;Post_2012 \u0026times; Industry-adjusted ROA\u003c/em\u003e ( \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.754, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in column (4) affirms the moderating effect of ETC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDeterrence Effect of Anticorruption Campaign\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.226\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.229\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.229\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.483\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.486\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.515\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.469\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.499\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.607\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.449)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.464)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.454\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.454\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.452\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.452\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.449\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.421\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.420\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.445\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.114)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.738\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.742\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.765\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.741\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.313)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.620\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.605\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.500\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.616\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.510\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.204)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.675\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.677\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.387\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.673\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.380\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.984\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-9.406\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.584)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5.040)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOfficial tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost_2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.221\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.471\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.128)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost_2012 \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times;Post_2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.544\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.552\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.094)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs \u0026times;Post_2012 \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.024\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.024\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times;Post_2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(7.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(7.029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.963\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.550)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.550)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC \u0026times;Post_2012 \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.754\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.776\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.775)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Province fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.509\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.342\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.090\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.471\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12,270. This table examines the deterrence effect of the anticorruption campaign. We examine subsidy granting decisions by repeating the regression of Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for various subsamples. We use 2012 (the start of the anticorruption campaign) as the cutoff year and focus on PPS made in the three years before (2009 to 2011) versus three years after (2013 to 2015). We estimate the regression for the whole sample. However, political embeddedness represents the characteristic of corrupted official, it thus doesn\u0026rsquo;t fit this model. Standard errors are in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e about here---\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eChannel Test\u003c/h2\u003e\u003cp\u003eWe examine the underlying logic of the testable hypothesis in terms of internal attribution tendency and the influence of political connection on CEO pay.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eAnticorruption and Internal Attribution Tendency\u003c/h2\u003e\u003cp\u003eWe posit that boards might lean towards attributing fluctuations in corporate performance to internal efforts rather than external factors. Consequently, they exhibit a pronounced internal attribution tendency. According to Shi, Chen, and Li (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003einternal attribution tendency\u003c/em\u003e is measured as the ratio of the number of internal attribution words to the number of both internal and external attribution words. Meanwhile, a higher frequency of first-person pronouns in causal attribution statements within MD\u0026amp;A indicates a marked internal attribution tendency. The list of words pertaining to internal attribution includes: I, Id, I\u0026rsquo;d, I\u0026rsquo;ll, Im, I\u0026rsquo;m, Ive, I\u0026rsquo;ve, me, mine, my, myself, lets, let\u0026rsquo;s, our, ours, ourselves, us, we, we\u0026rsquo;d, we\u0026rsquo;ll, we\u0026rsquo;re, weve, we\u0026rsquo;ve. the list of words pertaining to external attribution includes environment, demand, supplier, supply, customer, client, consumer, government, regulation, economic, economy, market, competitor, rival, competition, weather, disaster, industry (Shi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe present the findings in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The coefficient of \u003cem\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/em\u003e is positively and significant ( \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the boards are more likely to internally attribute performance when corrupted official are ousted.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChannel Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternal attribution tendency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eIndustry-adjusted CEO pay\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.781\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.110\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.262\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.262\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.888\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.886\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.508\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.509\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.307)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEO duality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.605\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.605\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState monopoly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.452\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm dependence government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.769\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.773\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.207)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical contestability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.393\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.392\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.444\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.771\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.728\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.413)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.413)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical embeddedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.071\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.135\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement \u0026times; Industry-adjusted ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical connection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.166\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm exposure to anticorruption enforcement \u0026times; Political connection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.105\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.061)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry/Year/Province FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.548\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9.554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.353)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e503.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e472.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,734. The table presents the results of mechanism test. Model 1 presents the results for internal attribution tendency. Internal attribution tendency is measured as the ratio of the number of internal attribution words to the number of both internal and external attribution words. Model 2 and 3 represents the results of political connection on CEO pay. Political connection equals to one if CEO or chairman was a number of People\u0026rsquo;s Congress or the Chinese People\u0026rsquo;s Political Consultative conference, or has been an officer of the central or regional government, zero otherwise. Standard errors are in parentheses. \u003csup\u003e+\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e---Insert Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e about here---\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eThe Influence of Political Connection on CEO Pay\u003c/h2\u003e\u003cp\u003eIn addition, we assess the influence of political connection on CEO pay. We suggest that ousted corrupt officials after anticorruption shocks leads to a breakdown in the CEO's external relationships, which in turn makes the market more efficient. Therefore, political connections are associated with higher CEO pay, but this relationship is weakened after anticorruption shocks. According to Xiang et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003epolitical connection\u003c/em\u003e equals one if the CEO or chairman was a number of People\u0026rsquo;s Congress or the Chinese People\u0026rsquo;s Political Consultative conference, or had served as an officer of the central or regional government, and zero otherwise. The results reported in columns (2) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e provide supporting evidence. We find that the effect of \u003cem\u003epolitical connection\u003c/em\u003e on \u003cem\u003eCEO pay\u003c/em\u003e is positive ( β\u0026thinsp;=\u0026thinsp;0.166, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the effect of \u003cem\u003eFirm exposure to anticorruption enforcement \u0026times; political connection\u003c/em\u003e on \u003cem\u003eCEO pay\u003c/em\u003e is negative ( β = -0.105, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). These results suggest that boards appear to shift their attributions about CEOs\u0026rsquo; performance from external to internal factors after anticorruption shocks.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Contributions\u003c/h2\u003e\u003cp\u003eFirst, our study extends research on the antecedents of CEO PPS, an important topic in CEO compensation research. Most previous research has adopted the agency perspective that CEO PPS is to resolve the principal-agency issue, and has overlooked the examination of PPS issues from the perspective of institutional environmental changes. Our study takes a novel approach by highlighting the role of ousted corrupt officials in influencing CEO PPS, we thus underscore the significance of considering broader institutional dynamics and their impact on executive compensation structures. By doing so, we contribute to a more comprehensive understanding of the intricate factors shaping CEO PPS.\u003c/p\u003e\u003cp\u003eSecond, we contribute to the literature on outcomes of public governance. Anticorruption is a type of public governance, and prior research has mostly examined its influence on macroeconomics, financial markets, corporate market strategies, and non-market strategies. Our research shifts the focus to examine its influence on CEO PPS, which serves as an important mechanism for efficient corporate governance.\u003c/p\u003e\u003cp\u003eThird, our study extends the literature on political ties by differentiating between two distinct forms of firms’ political connections. We categorize these connections as ascribed business-government connections (pertaining to SOEs) and achieved business-government connections (linked to ETC), and find that these two types of political ties play different roles in moderating the effect of anticorruption on CEO PPS. As such, we contribute to the advancement of literature by theorizing on their unique contextual roles in influencing the relationship between anticorruption initiatives and CEO PPS.\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003ePractical implications\u003c/h2\u003e\u003cp\u003eThis paper highlights key practical and policy implications from the study of anticorruption campaigns on corporate governance. It emphasizes that government-led anticorruption efforts significantly boost political institutions and reduce firms’ dependence on government, encouraging shifts towards internal pay-for-performance mechanisms. This underscores that anticorruption initiatives are effective tools for improving corporate governance and addressing principal-agency issues. With regard to managerial implications, environment changes can greatly affect the effects of corporate strategies (Tan \u0026amp; Tan, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In light of pervasive political uncertainties stemming from scandals, national elections, political turnover, global summits, and institutional changes (Julio \u0026amp; Yook, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), corporate political strategy necessitates a comprehensive evaluation from a broader perspective. Managers of non-SOEs or firms characterized by high ETC, navigating through increasingly unpredictable political landscapes, must remain vigilant to potential risks associated with political shocks. Importantly, such shocks may result in more significant disruptions for non-SOEs compared to SOEs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSION AND LIMITATIONS","content":"\u003cp\u003eTo investigate how firm exposure to anticorruption enforcement affects CEO PPS, this study employs ordinary least squares regression, using a sample of 3,215 Chinese A-share listed firms from 2012 to 2018. The empirical results show that (1) there is a positive association between firm exposure to anticorruption enforcement and CEO PPS, a conclusion that remains robust after robustness checks including parallel trend analysis, placebo tests, PSM-DID, alternative measures of dependent variables, and alternative meaure of anticorruption shock. (2) This positive association is weaker for SOEs, but stronger for firms with higher ETC and for firms located in provinces where the political embeddedness of ousted corrupt officials is stronger. (3) Moreover, the channel test reveals that boards appear to shift their attributions about CEOs’ performance from external to internal factors after anticorruption shocks.\u003c/p\u003e\u003cp\u003eOur study also has several limitations, which point to potential directions for future research. First, this study dose not involve a direct measurement of board attributions. Our assertion is that boards formulate internal attributions concerning firm performance, influenced by anticorruption measures. The removal of corrupt officials prompts board members to form precise evaluations regarding the CEO’s responsibility for overall company performance. Nevertheless, firm performance is the outcomes of CEO’s ability and effort, but our data do not provide insights into whether boards base their decisions on CEO compensation by assessing the CEO’s ability or effort. Future research could implement the field studies and experiments through offering more detailed insights into board attributions.\u003c/p\u003e\u003cp\u003eSecond, despite exploiting the staggered timing of provincial-level removals, our design cannot fully rule out all forms of time-varying provincial shocks or other concurrent institutional changes that might influence both firm governance and pay practices. In addition, our primary exposure measure, a three-year post-event indicator for provincial removal, is an operational choice; sensitivity to alternative window lengths could be explored in future work.\u003c/p\u003e\u003cp\u003eThird, the measures of political ties (e.g., abnormal ETC and official tenure) capture particular dimensions of business–government linkage but do not exhaustively cover all forms of political capital. Future studies could explore additional forms of political connections in the relationship between anticorruption and CEO PPS.\u003c/p\u003e\u003cp\u003eFinally, our sample is limited to publicly listed Chinese firms; therefore, caution is warranted in generalizing to private firms or other national contexts. Future studies can test our findings in other institutional contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is supported by the National Natural Science Foundation of China (Grant No. 72102003; 72091310; 72091314; 72102184; 72302068; 72402170); National Social Science Foundation of China (Grant No. 21\u0026amp;ZD137); Humanities and Social Science Project, Ministry of Education of China (Grant No. 23YJC630195; 21YJC630128); Shaanxi Social Science Foundation (Grant No. 2023R023); Starting grant from Peking University; Fundamental Research Funds for the Central Universities (Grant No. HIT.HSS.202301).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnokhin, S., \u0026amp; Schulze, W. 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Emerald Group Publishing Limited.\u003c/li\u003e\n\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CEO pay-for-performance sensitivity, ousted corrupt officials, firm exposure to anticorruption enforcement, internal attribution, anticorruption","lastPublishedDoi":"10.21203/rs.3.rs-7477999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7477999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe leverage an exogenous shock\u0026mdash;the ousted corrupt officials in anticorruption campaign\u0026mdash;and examine how the CEO pay-for-performance sensitivity (PPS) was affected. We argue that the staggered removal of provincial officials on corruption charges during China\u0026rsquo;s anticorruption campaign leads to increased firm exposure to anticorruption enforcement, prompting firms to transition from external to internal attribution. We suggest that the firm exposure to anticorruption enforcement (ousted corrupt officials in firm\u0026rsquo;s location) will increase CEO PPS in a firm. Furthermore, we examine how the effect of firm exposure to anticorruption enforcement on CEO PPS varies across different levels of political involvement. Our analysis reveals that the positive association between firm exposure to anticorruption enforcement and CEO PPS is attenuated for state-owned enterprises (SOEs). Conversely, this relationship is amplified for firms with greater reliance on entertainment expenses or when the political embeddedness of ousted corrupt officials is more pronounced. This study makes significant contributions to the literature on CEO PPS and business-government connections.\u003c/p\u003e","manuscriptTitle":"From external to internal: The influence of ousted corrupt officials on CEO pay-for-performance sensitivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 10:10:05","doi":"10.21203/rs.3.rs-7477999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-01T11:07:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11171304922748762262986409958209327694","date":"2026-02-24T09:20:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T02:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241989591174167720724825331335256819190","date":"2025-11-04T14:17:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-17T15:01:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-11T04:47:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T11:42:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-21T14:10:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-21T14:06:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1060c8bc-eff8-4f89-8c19-8b879466115b","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56990013,"name":"Business and commerce/Business and management"},{"id":56990014,"name":"Social science/Business and management"},{"id":56990015,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-10-30T10:10:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 10:10:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7477999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7477999","identity":"rs-7477999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}