在需求收缩、供给冲击和预期转弱三重压力叠加的复杂形势下,妥善处置地方政府债务问题、牢牢守住不发生系统性风险的底线是确保当前我国经济稳定和发展安全的关键。文章以2010-2020年我国非金融上市公司年度数据为样本,采用2015年新《预算法》的正式施行作为外生政策冲击构造准自然实验,深入考察了地方政府债务治理对实体企业系统性风险的影响效应及作用机制。研究结果表明,地方政府债务治理通过杠杆失衡机制和信用关联机制,有效降低了实体企业的信贷错配、过度负债及杠杆操纵水平,显著弱化了实体企业间的担保关联和因影子银行与金融化活动引致的信用风险关联,从而有力缓释了实体企业的系统性风险。这一效应在政府隐性担保强、金融监管程度弱及城投债务期限长的地区尤为明显。经济后果上,地方政府债务治理对实体企业系统性风险的积极缓释效应,最终有利于促进企业经营业绩的平稳增长。文章的研究揭示了地方政府债务治理具有防范化解系统性风险以及消除经济增长隐患的重要政策效果,对于深化地方政府债务治理改革和防范化解重大经济金融风险具有重要理论启示。

近年来,中国扶贫事业取得了巨大成就,系统解构其实现路径具有重要的理论与应用价值。文章从国有资本参股民营企业的视角,考察产权混合所有制改革对于企业扶贫参与的促进作用。文章立足于中国民营企业存在大量国有股权的基本现实,根据股权结构影响企业行为的基础逻辑,系统分析了国资参股对民营企业扶贫参与的积极影响及其内在机理。基于2016-2020年A股民营上市公司的样本,文章实证研究发现,国资参股明显地促进了民营企业的扶贫参与,在一系列稳健性检验后该结论仍成立。机制检验发现,国资参股通过提升民营企业的资源基础和强化民营企业对扶贫社会责任的价值认同而产生作用。进一步分析发现,该作用在有同省份国有资本参股的民营企业、政府干预较强地区及贫困程度较高地区的民营企业中更为明显。文章探索了我国企业混合所有制改革与精准扶贫之间的学理关系,从理论上丰富了混合所有制改革成效的研究,为“十四五”时期巩固扶贫成果以及深化混合所有制改革提供了重要的政策依据,也为其他发展中国家探索反贫困道路提供了有益借鉴。

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.