This study contributes to the understanding of the relationship between Chinese local government debt and house prices by proposing the land channel as a novel explanatory framework. We construct a three-sector equilibrium model and demonstrate that local government debt positively affects house prices through both direct and indirect effects, with the indirect effect operating through the land market. However, the land use efficiency mitigates the positive effect of government debt on land and house prices within indirect effect. These propositions are empirically confirmed using a panel dataset of 260 cities in China from 2011 to 2019.

This study investigates the influence of mobile payment application use on crime rates. Using a unique database of verdicts from criminal courts in China and an index measuring the extent of mobile payment usage, we find that a one standard deviation increase in mobile payment adoption and usage leads to an 11% decrease in the theft rate. Furthermore, the effect of widespread mobile payment adoption on theft rates is more pronounced in areas characterized by a higher prevalence of cash transactions. These findings suggest that the decrease in cash circulation in society due to mobile payment use can reduce incentives for theft. However, we do not find evidence linking mobile payment usage to other types of criminal activity, including robbery, arson, brawling, homicide, and serious injury by vehicle.

In this paper, we examine the prediction performance using a principal component analysis (PCA). In particular, we perform a PCA to identify significant factors (principal components) and then use these factors to form predictions of stock price movements. We apply this strategy on the Chinese stock markets. Using data from January 2, 2019 till September 16, 2021, the empirical results show substantial out-performances from the PCA-based predictions against a naïve buy-and-hold strategy and also single time-series predictions of individual stocks. Next we examine if the factors retrieved from PCA are indeed important contributing factors in explaining stock price movements. To do this, we adopt a machine learning technique popular in studying stock performances – random forest. We discover that, comparing to widely used descriptive factors such as industry sector, geographical location, and market types (known as “board” or “ban” in Mandarin), principal components rank very highly among those descriptive factors.

We examine the effect of a regulation in China that restricts perquisite consumption by managers of state-owned companies. We find that the regulation causes state-owned companies to issue more entrusted loans to other firms. Furthermore, entrusted loans issued by state-owned companies have lower interest rates and larger loan amounts. These results suggest that managers of state-owned companies use entrusted loans to extract personal benefits to compensate for the lost perquisite consumption due to the regulation.

Quantifying the economic loss from climate change in China is crucial for understanding the potential costs and benefits of climate policy within the context of carbon neutrality. This study develops a multidisciplinary and integrated assessment framework for climate damage, which uses the Beijing Climate Center Simple Earth System Model (BCC-SESM) to estimate climatic data under the Representative Concentration Pathways (RCPs) scenarios with the medium Shared Social-economic Pathway (SSP2) scenario in China. This paper estimates climate damage in eight major sectors by a bottom-up approach, makes substantive revisions and calibrations for the sectoral climate damage functions and parameters for China based on the FUND model, and formulates the aggregate climate damage function. Results show that under the Business-as-Usual RCP8.5 scenario, by 2050 human health damage accounts for the largest share (61.92%) of the total climate loss, followed by sea-level rise damage (18.57%) and water resources damage (5.84%). Climate damage in non-market sectors reaches 14.64 trillion CNY, which is a 4.8-fold increase over the climate damage of market sectors which is only 3.02 trillion CNY. The total climate damage function for China is a quadratic function of temperature rise, with climate damage of 5.36%, 5.67%, 5.74%, and 8.16% of the GDP by 2050 under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 respectively, indicating that the marginal climate damage increases non-linearly with temperature rise.

We estimate the causal effect of income on happiness using a unique dataset of Chinese twins. This allows us to address omitted variable bias and measurement errors. Our findings show that individual income has a large positive effect on happiness, with a doubling of income resulting in an increase of 0.26 scales or 0.37 standard deviations in the four-scale happiness measure. We also find that income matters most for males and the middle-aged. Our results highlight the importance of accounting for various biases when studying the relationship between socioeconomic status and subjective well-being.

This study examines the relationship between analyst forecast dispersion or accuracy and supplier concentration of listed firms in China from 2008 to 2019. Our findings suggest that higher supplier concentration is associated with lower analyst forecast dispersion, which can be attributed to the increased attention it receives from analysts. Moreover, this effect is more pronounced when firms have less bargaining power and higher institutional ownership, indicating a greater reliance on the supply chain. Our study highlights the importance of disclosing supply chain information, which provides insight beyond traditional financial information.

Evidence from the U.S. and Europe suggests that current risk assessment tools used by researchers and financial professionals to determine individuals’ risk tolerance and provide suitable portfolio recommendations may be flawed due to “mis”perceptions of risk. Limited research has examined the reliability of these tools as measures of relative risk tolerance for households in emerging economies like China. This study develops a multidimensional index of risk tolerance specifically tailored for Chinese households using a psychometric approach. The effectiveness of this multidimensional index in predicting individuals’ financial decisions is tested and compared to traditional unidimensional measures of risk tolerance commonly used in developed countries. The findings indicate that multidimensional measures are more consistent and significant predictors of Chinese households’ investment decisions. Additionally, the study uncovers evidence that cultural differences, related to market expectations and social networks, which are often overlooked in U.S. and European models, play a crucial role in shaping individuals' risk perceptions and investment choices in China. Robustness checks were conducted to account for potential endogeneity between risk tolerance and investment decisions. The findings provide valuable insights for researchers and financial professionals seeking to develop more accurate risk assessment tools that capture risk attitudes and perceptions in China and other developing countries. By adopting a multidimensional approach that accounts for cultural and psychosocial factors, these improved tools can enhance the precision of risk evaluation and facilitate more appropriate investment recommendations.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

This study investigates the association between the expansion of formal finance through the opening of high-speed railway (HSR) and the risk of private lending in China, using data from China Judgments Online. The study employs a difference-in-differences approach and reveals that the cities connected by HSR have a greater risk of private lending and that the formal finance expansion leads to a cream-skimming effect on private lending. The HSR enhances the efficiency of product supply and pricing of formal financial institutions, which results in the expansion of banks. However, private lending relies heavily on social networks, which limits the direct effect of HSR on it. Consequently, the formal finance expansion facilitated by HSR has a significant cream-skimming effect on private lending, which increases the risk of private lending. These results make a significant contribution to the existing literature on the economic implications of formal financial expansion for private lending.