就业是最基本的民生,农民工的高质量充分就业是城乡融合发展与全体人民共同富裕的重要内容,而农民工作为弱势群体,突发性冲击会对其就业产生巨大的影响。文章基于“突发性冲击-农民工流动-农业再就业”的逻辑框架,利用中国家庭追踪调查数据,采用广义双重差分模型论证了突发公共卫生事件对农民工的工资收入、就业地点以及农业就业的冲击效应。研究发现：在重大突发公共卫生事件冲击下,劳动力供需变化与市场配置效率下降加大了农民工工资下行压力,带来了不利的收入效应；加之不确定性风险较高,农民工外出务工的机会成本增大,县域内就业增加,农民工流动性下降；由于非农就业机会有限,农民工利用农业的劳动力蓄水池功能进行农业再就业,获得暂时的就业保障以抵御公共卫生风险。此外,城市的失业效应与劳动者的替代效应是突发性冲击影响农民工就业选择的潜在机制。据此,文章认为将更多农民工纳入就业保障体系,大力发展县域经济,推动农业现代化与农村产业融合,以及通过技能培训等加强对弱势农民工群体的救助和支持是保障农民工生计、提升农民工就业质量的重要途径。

Using data on Chinese firms with the unique state ownership structure of stateowned enterprises (SOEs), we examine whether institutional investors can help reduce the required returns on equity for SOEs or non-SOEs, and if so, the underlying channels. We find that an increase in the shareholdings of institutions, especially independent institutions, can reduce the required returns. This effect is more prominent in non-SOEs than in SOEs, indicating that state ownership may limit the effect by which institutional investors reduce the required returns. In addition, institutional investors promote corporate social responsibility in invested firms and may thereby reduce the required returns on equity.

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.