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This book is an empirical study on the relationship between private enterprises, entrepreneurs and the government in P. R. China. The two authors conducted a detailed survey of enterprises and entrepreneurs in Liuzhou, Guangxi Zhuang Autonomous Region, China. Although it was only conducted in a medium sized city, the survey provides a rare source of information on matched entrepreneur-enterprise pairs. It provides detailed information on management, performance, enterprise-government relationship, as well as entrepreneurs' personal information and measurements of various psychological parameters. With this first-hand information, the authors analyzed several interesting issues concerning enterprise-entrepreneur-government relationships. Readers will gain an understanding of the following topics: Why and how does China have such special enterprise-entrepreneur-government relationships? Do enterprises' political connections in the form of entrepreneurs' political status help improve the performances of these enterprises? Which of the surveyed entrepreneurs could become members of the People's Congress and the People's Political Consulting Conference? How do entrepreneurs feel when they are faced with greater government intervention? How will China move ahead in the ongoing reform and development in the light of the enterprise-entrepreneur-government relationship? This book examines the way in which China's enterprise-entrepreneur-government relationship helps enterprises develop in a transitional market. In the appendix to this book, one of the authors, Ming Lu, provides evidence, based on data from listed companies, that having political connections can help enterprises enter the markets of provinces other than their place of registration. However, this political connection also distorts the market by giving the entrepreneurs more opportunities to develop their business. At the same time, those entrepreneurs who face interventions from the government also shoulder greater costs in the form of loss of psychological happiness. The inference of this book is that at some point in the foreseeable future, China will gradually build its market system and integrate its domestic markets, so that private enterprises will no longer rely so heavily on their political connections

Background Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. Methods and findings Population and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences. Interpretation Compared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics. ; Peer reviewed

AbstractResearch Question/IssueWe examine the role of corporate executives in dividend tunneling activity by controlling shareholders and whether the correlation between executive ownership and dividend tunneling is influenced by internal and external governance mechanisms.Research Findings/InsightsWe find increased executive ownership may lead to a higher level of dividend tunneling. This is further strengthened by our finding that the positive effect of executive ownership on dividend tunneling is more pronounced for firms with weaker minority shareholder protection. In addition, our results show that higher degrees of state ownership may further intensify this positive association. Finally, we find that analyst coverage has a moderating effect and constrains the collusion between controlling shareholders and executives in dividend tunneling activity.Theoretical/Academic ImplicationsOur study contributes to the literature on the role of managerial ownership in controlling shareholders' dividend tunneling activity. We fill a gap in the literature on the corporate agency problem by providing evidence that dividends have been employed by controlling shareholders as a means of tunneling and that executives with higher ownership are more likely to collude with controlling shareholders in dividend tunneling activities.Practitioner/Policy ImplicationsThis study contributes to the debates around the promotion of the cash dividend policy in China, as our findings show that cash dividends are used as a tunneling vehicle. Providing important evidence to regulators, our findings support the argument that external monitoring by financial analysts can effectively constrain dividend tunneling by dominant shareholders, especially in the context of emerging stock markets with high ownership concentration, weak minority shareholder protection, and an underdeveloped legal system.

Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges. ; Peer reviewed