This study explores how the application of democratic rule in land reform decision-making determines villagers’ political trust towards different levels of the government in China. Based on analyses of a two-period household survey data we find that in China’s most recent Collective Forest Tenure Reform, the use of democratic rule improves villagers’ trust for town and county cadres, whereas the impact on trust towards village cadres is only significant for the democracy involving all the villagers or households in a village. This pattern of trust is partly explained by our findings that the democratic process helped decrease the unresolved inter-village forestland disputes whilst there seems no such impact on the within-village land disputes. Heterogeneity analyses show that democratic decision-making has a more pronounced effect in improving trust for villagers with lower income, and those without affiliation with the Chinese Communist Party (CCP) or to the village committee.

Forming diversified global supply chains (GSC) is an important approach to improving economic resilience. When firms expand their oversea suppliers for such purposes, information friction is a major challenge, and internetization may help firms cope with it by more efficient communication of information. We introduce a dynamic discrete choice model for firms’ searching for new supplier sources estimated with structural methods, and construct counterfactual studies to analyze the internetization effects on Chinese firms’ GSC diversification. Our quantitative studies reveal that internetization relieves information friction, which reduces firms’ searching costs by 13.4%, and thus significantly diversifies firms’ GSC. It also raises firms’ productivity by 0.5% through efficient communication of information. Reductions in searching costs are revealed as the main channel of such effects of internetization, while the productivity channel is less significant. Moreover, the internetization effects on diversifying GSC are persistent over time, and are biased towards high-productivity and importing firms.

The discussion on the innovative effects of intellectual property pledge financing is a mainstream trend. In this context, this study has improved the existing research from several aspects, such as broadening the dimensions of innovation, adding dynamic analysis, refining multidimensional mediation mechanisms, and employing unique samples. Ultimately, we come to the following conclusions: (1) Intellectual property pledge financing suppresses enterprise innovation, especially innovation quality, but this pattern will be broken by raising the threshold of innovation conditions. The reason is that strict innovation conditions can lead to a poor innovation foundation for enterprises, which are rarely affected by the fluctuation of funds obtained from intellectual property pledge financing. (2) Intellectual property pledge financing has a non-linear effect on firm innovation, characterized by an increase followed by a decrease, suggesting that intellectual property pledge financing in current China can only provide a temporary stimulus for firm innovation. (3) The relationship between intellectual property pledge financing and enterprise innovation is strongly moderated by the ownership, type, and size of the enterprise, with the inhibitory effect of intellectual property pledge financing on enterprise innovation occurring mainly in state-owned enterprises, high-tech enterprises, and small enterprises, while its positive effects are more pronounced in private enterprises, non-high-tech enterprises, and medium-sized enterprises. (4) Financing constraints, internal incentives, external supervision, and signaling mechanisms are indeed key pathways through which intellectual property pledge financing affects firm innovation, especially when we analyse these mechanisms using dynamic models.

Between high-quality and high-efficiency green innovation, which can truly reduce stock price crash risk? We use data from Chinese listed companies from 2010 to 2022 to study the impact mechanism and effect of radical and incremental green innovation stock price crash risk. Results show that radical green innovation can significantly reduce stock price crash risk, and this effect is more evident than the incremental one. Radical green innovation can improve information efficiency and enhance risk management, thus reducing stock price crash risk. Besides, among companies held by trading institutions and with low analyst coverage, the inhibitory effect is more evident.

ESG is emerging as a new benchmark for measuring a company's sustainable development capabilities and social impact. As a measure of ESG performance, ESG ratings are increasingly receiving attention from companies, the general public, and government institutions, and are becoming an important reference factor influencing their decision-making. This paper investigates the impact of corporate ESG ratings on Total Factor Productivity (TFP) and its mechanisms of action. Focusing on listed companies in China, we find that higher ESG ratings contribute to improving a company's TFP, and this conclusion remains valid after robustness tests and addressing endogeneity issues. Further exploration into the reasons behind this result reveals that ESG ratings can be seen as a signal that a company sends to the outside world, representing its overall performance. Higher ESG ratings enhance a company's TFP by reducing market financing constraints and obtaining government subsidies. Heterogeneity analysis shows that the positive impact of ESG ratings on TFP is more pronounced for companies with higher levels of attention, reputation, and audit quality. Additionally, we explore whether ESG ratings can serve as a predictive indicator for measuring a company's TFP. This hypothesis was tested using machine learning algorithms, and the results indicate that models incorporating ESG rating indicators significantly improve the accuracy of predicting a company's TFP capabilities.

We investigate the effect of third-party certification for green bonds by analyzing its impact on issuer's future green innovation performances. We find that third-party certification for green bonds can significantly promote issuer's future green innovation performances. Furthermore, the promotion effect is more prominent in non-state-owned issuers, large issuers and heavy polluting issuers, and can be more significantly exerted by professional and reputable third-party certification agencies. Besides, third-party certification for green bonds can play the effect by reducing the issuer's tax expenditure, increasing the issuer's loan financing, and receiving a positive response in stock returns. But unexpectedly, it cannot play the effect by further reducing the credit spread of green bonds. Our findings indicate that independent external supervision can play a positive role in green bond issuance, but there is still a long way to go.

Using firm-customer matched data from 2005 to 2020 in China, we examined the spillover effects and mechanisms of green innovation (GI) among supply chain partners. Results show a positive association between customers' GI and their supply firms' GI, indicating spillover effects in the supply chain. Customers' GI increase from the 25th to the 75th percentile leads to a significant 19% increase in supply firms' GI. Certain conditions amplify the spillover effect, including customers with higher bargaining power, operating in less competitive industries, and supply firms making relationship-specific investments or experiencing greater customer stability. Geographic proximity and shared ownership further enhance the spillover effect. Information-based and competition-based channels drive the spillover effect, while customers with higher GI encourage genuine GI activities by supply firms. External environmental regulations, such as the Chinese Green Credit Policy and Environmental Protection Law, strengthen the spillover effect, supporting the Porter hypothesis. This research expands understanding of spillover effects in the supply chain and contributes to the literature on GI determinants.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

We propose a sufficient statistics approach to measuring Ricardian comparative advantage in a quantitative trade model featuring cross-country differences in productivity, factor prices, market size, as well as monopolistic competition, endogenous markups, and firm heterogeneity. The model’s micro-foundations do not necessarily imply that the relevant data for the proposed sufficient statistics must include micro information, but its micro-structure is needed to understand how only macro information can be used instead. Applying the approach to Chinese microdata and cross-country macrodata, we show that imperfect competition with endogenous markups and firm heterogeneity have far-reaching implications for correctly measuring Ricardian comparative advantage.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.