Using the regression discontinuity design, a quasi-experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.

Government subsidies are often used to stimulate environment-friendly investment. We find that Chinese firms reduce green investment as the uncertainty of subsidies rises. This effect is identified from weather-driven fluctuations in air pollution that lead to fluctuations in subsidy allocations: Firms in cities where weather-driven subsidy uncertainty is high engage in less green R&D investment, patent applications, and research staff. Industries that are heavy emitters and those focused on environmental technologies are more affected. The results suggest that policy uncertainty may originate not only from political and macroeconomic shocks but from behavioral mechanisms that link policy to salient recent conditions.

The New Cooperative Medical Scheme (NCMS) rolled out in China from 2003-2008 provided insurance to 800 million rural Chinese. We combine aggregate mortality data with individual survey data, and identify the impact of the NCMS from program rollout and heterogeneity across areas in their rural share. We find that there was a significant decline in aggregate mortality, with the program saving more than one million lives per year at its peak, and explaining 78% of the entire increase in life expectancy in China over this period. We confirm these mortality effects using micro-data on mortality, other health outcomes, and utilization.

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.