China has emphasized promoting common prosperity in recent years to narrow the wealth inequality. Based on channels identified by Auclert (2019), this project investigates the impact of China’s monetary policy on households’ wealth levels and wealth distribution using data from the China Family Panel Studies (CFPS) during 2012-2018. Analysis shows that: (1) household wealth is more affected by real interest rate adjustments than unexpected inflation; (2) the impacts of monetary policy loosening and tightening cycles on household wealth are not symmetric and a persistently accommodative interest rate policy may do more harm than good to social wealth; (3) financial development raises the sensitivity of household wealth to monetary policy adjustments with heterogeneous effect across different channels.

Procedures used for many school choice programs and centralized college admissions require the participants to submit a ranking over all the schools or colleges. The widespread use of the Internet, however, allows for new methods for determining these assignments through the use of sequential mechanisms. In these, participants choose their desired outcome from the options available, which are updated accordingly, before a final matching is produced. This allows students to focus their applications away from institutions that will not accept them, given other students’ choices. In this project, we evaluate sequential mechanisms currently being used to match millions of students to universities and proposed alternatives, testing their theoretical and empirical properties and the extent to which they help students make better choices and improve assignments. We will also provide a new mechanism to be used in these problems, which combines contemporaneous choices with historical statistics to produce assignments.

This project aims to assess how the career incentives of local leaders in China impact the entry and exit dynamics of firms. Despite having relatively weak formal institutions, China has achieved impressive economic growth. Emerging studies suggest that this may be attributed to the existence of a second-best, informal institution setup, where local politicians in China, facing the regional decentralized tournament system, are incentivized to promote economic development to advance their personal careers. Through the lens of firm entry and exit, we investigate both the advantages and limitations of this special institutional arrangement, contribution to the important literature that explores the institution-development nexus.

The Shanghai (Shenzhen) -Hong Kong “Stock Connect” program allows investors in mainland China and Hong Kong residents and foreign investors to trade eligible stocks listed on the other market, through the exchange and clearing houses in their home markets. This program, announced in April 2014 and begun in November 2014, is regarded as a major step toward internationalizing China’s security markets.

The project analyzes the effects on Chinese firms of the “China Connect” equity market liberalization. Because China is a capital abundant country, unlike typical emerging markets in the literature, the benefits and costs of liberalization are logically different. Nonetheless, the liberalization brought benefits: lower funding costs, higher stock prices, and more investment for connected firms compared to unconnected firms, despite a common negative effect on all firms from capital outflows. These benefits come from a new channel: reducing domestic credit misallocation between private- and state-owned enterprises. The project also documents costs: connected firms became more sensitive to external shocks than unconnected firms.

Research and development (R&D) policy differs from other policies in its various forms and easy implementation. R&D policy may also vary substantially across countries and regions. There is no consensus in the literature about the effectiveness of R&D policy on promoting technology transfer and stimulating economic growth. Exploring this problem not only contributes to the theoretical literature, but also helps designing long-run policy systems that increase technological innovations and facilitate the growth process. This project focuses on two regimes of R&D policy: patent policy and subsidy policy, to systematically study the mechanisms behind which these policy regimes affect technology transfer and economic growth. First, based on cross-country data, this project will analyze summary statistics regarding R&D policy, technology transfer, and economic growth to identify the important roles of R&D policy under different growth frameworks. Second, according to the steady-state and dynamic features of R&D policy, dynamic general equilibrium frameworks with endogenous growth will be constructed to characterize the behaviors of households, firms, and governments. Then by using methods of numerical dynamic programming and empirical moments matching, combined with macroeconomic database, the model is solved analytically and numerically in addition to calibrating parameters. Finally, the calibrated parameters will be used to perform quantitative simulations about the impacts of patent design and subsidization setup on technology transfer, economic growth, and social welfare, respectively. The simulated outcomes will provide qualitative implications that evaluate policy alternatives for their implementation.

Differences exist in the laws between Mainland China and Macau SAR on cross-border data flow. To ensure the legal compliance of cross-border data flow has become a prominent issue faced by businessmen in mainland China and Macau SAR in their business transactions. In this case, by seizing the opportunity of developing the Hengqin In-depth Cooperation Zone, it will be an effective solution to establish a data circulation base in Hengqin to enable businesses to exchange their data. The establishment of such a base must be dependent on the laws of the two sides. The legal system in Macau has strong historic origin of and high similarities with Portuguese-speaking countries. Therefore, before studying the cross-border data flow regulation between Mainland China and Macau SAR, it is necessary to make an understanding of the relevant legislations of Portuguese-speaking countries. At the same time, data legislations in the European Union, the United States and some other countries, are earlier than that of China, and have certain international influential power. Their mature experiences in data legislation can also provide a reference for the research of this project.

Arbitrators are generally obliged to perform their duty to solve commercial disputes independently and impartially. When they fail to comply with this duty they may be legally liable for their misconduct. This liability usually assumes the nature of contractual or tort liability. By comparison, criminal liability of arbitrators is rarer, at least at the practical level. In this aspect, China stands as an outlier, not only having established a set of criminal provisions to regulate arbitrators, including specific provisions regarding bribery of arbitrators and a crime named “perversion of law” in 2006, but indeed there have been a number of cases where defendants have been convicted as a result of these provisions. This approach was received with skepticism by some Chinese and foreign legal practitioners who warned that this approach could discourage the usage of arbitrators in China and that it would give police and court authorities overbroad powers to intervene in arbitral proceedings.

Considering the importance of arbitration in China and world trade, this research will focus on reviewing the application of the aforementioned legislation in the period 2006-2023. A combination of data analysis and judgement content analysis is performed to discover how arbitrators in China are criminalized, including an identification of key players in prosecuted cases, crime patterns and punishments. Based on the data, the study offers insights into the impacts of criminal legislation on arbitrators and to what extent, if at all, this legislation has been used to disrupt arbitral proceedings.

For financial index tracking, a sparse tracking portfolio with only a small number of assets is often desirable in practice in order to avoid small and illiquid positions and large transaction costs. The tradition way of using Cardinality constraints to directly to limit the number of stocks is if often difficult and computationally intensive as the resulting optimization problem is NP hard. Owing to its computational efficiency and variable selection properties, this project employs the regularization technique originating from high-dimensional statistics for sparse index tracking in high dimensions.

The financial activities in the metaverse are essentially the decentralized finance (DeFi), which is based on the blockchain system. The security of DeFi, therefore, is critical for the future development of metaverse-related economic industries, especially the metaverse tourism. However, by April 2022, a financial loss of more than $3.24 billion USD has been caused by a vulnerability in the smart contracts that make up financial apps. Blockchain companies who perform audits of DeFi applications can find smart contract logic flaws and interactions with other DeFi entities. However, the previous studies discovered that many accidents occurred on DeFi applications that had been audited, indicating that the quality of services provided by DeFi audit companies varies. This research aims to explore DeFi audit companies’ potential to resolve the loss in DeFi incidents and examine legal issues associated with using DeFi auditing services. This study will promote a safer cryptocurrency industry in the Asia-Pacific region, where virtual assets have been identified as key growing economies.

The question “What makes a tourist destination competitive?” is one of the central questions in tourism and hospitality management. Understanding how tourist destinations perform and what makes them competitive is important for tourists and all the stakeholders involved, including residents, tourism practitioners, and policymakers (Andrades et al., 2017). Significant efforts in helping tourism destinations evaluate and measure their competitive advantage compared with that of other destinations worldwide were exerted over the past three decades (Xia et al., 2019; 2020). Yet, a number of theoretical and methodological issues remain, despite the great interest in the topic by tourism scholars. One key question relates to the epistemological underpinnings of “Who defines what makes the tourist destination competitive “?
This project aims to address this question to better understand tourist destination competitiveness to ultimately improve strategic positioning of destinations. We use Macao as a Case Study context and propose to adopt innovative AI methods, using user-generated data to both conceptually and methodologically advance the understanding of ‘what makes tourist destinations competitive,’ which can then be extended with complementary qualitative analysis to create an impact on improving tourist destination competitiveness. Importantly, we also address the current highly significant real-world problem to ensure long-term recovery to ensure the prosperity of Macao businesses, residents, and future tourists alike.

Tourism contributes significantly to a region’s economic and business development, while the growth infrastructure of the region can also influence the tourism industry indirectly. Recently, with the methodological development on tourism demand forecasting, the interests of researchers have been shifted from traditional time series forecasting and econometric models to Artificial Intelligence (AI) models. Many works have incorporated deep learning models into tourism demand forecasting by analyzing bid data collected from the Internet. However, these techniques are either predetermined on the selected data or directly leveraging the forecasting practice without clear understanding on the impacts of data characteristics. As such, it remains unclear for the relationship between data characteristics and the maximum predictivity in tourism demand. In the tourism industry, demand forecasting is an important way to support the practitioners in decision making, for which the interpretation on the forecasting at micro and macro levels is also important. This study aims to fill these two gaps by using the information theory and the Bayesian networks. We will propose an explainable predictivity tourism demand forecasting framework, which can provide an analysis of multi-variate predictivity and the interpretation while maintaining accurate forecasting.