Digital transformation (DT) is an ongoing and costly process that requires careful planning and the motivation of top executives (CEOs). This research analyze the CEOs compensation as a motivation to embrace DT by reducing agency issue. We determine the extent of DT through a textual analysis method and utilize data from Chinese publicly traded companies spanning the period between 2007 and 2020. Our study findings are threefold, (a) we observe a positive relationship between CEOs' pay gap and DT, highlighting the significant role CEOs compensation plays in encouraging CEOs to adopt digitalization, (b) we find that managerial shareholding significantly enhances this relationship, (c) we note that the relationship between CEOs pay gap and DT is more pronounced in state-owned enterprises compared to non-stateowned enterprises. Additionally, we discover through channel analysis that agency cost and audit quality mediate the relationship between CEOs pay gap and DT potentially by reducing the agency problem between CEOs and shareholders. These findings are vital for comprehending the pay practices and behaviors of corporate executives regarding digitalization in China. Importantly, the study results remain robust when considering instrumental variables (IV), propensity score matching (PSM), and alternative techniques.

This paper examines the effects of digital finance and corporate stock price crash risk, and the underlying mechanisms, using panel data from Chinese A-share listed companies between 2012 and 2021. Specifically, we focus on whether digital transformation and environmental, social, and governance (ESG) performance are intermediary channels through which digital finance mitigates corporate stock price crash risk. By employing panel regression and mediation effect models, we demonstrate that digital finance significantly reduces corporate stock price crash risk. This conclusion remains robust after a series of robustness tests, including the replacement of core explanatory variables, lagging digital finance by one period, using alternative dependent variables, applying the instrumental variables method, and system GMM estimation. More importantly, we find that digital finance curbs stock price crash risk by enhancing digital transformation and ESG performance. In addition, we reveal that digital finance has heterogeneous effects on corporate stock price crash risk. The inhibitory effect of digital finance on stock price crash risk is more pronounced in the central and western regions of China and for companies with lower internal control levels, higher information transparency, and higher financing constraints.

This study examines the impact of Shenzhen Stock Exchange’s (SZSE) information disclosure ratings on investment efficiency in China. Based on a sample of Chinese A-share listed companies on the SZSE from 2001 to 2018, we discover that superior information disclosure ratings improve investment efficiency after controlling for various firm- and industry-level variables. Our findings remain valid after various robustness tests and using instrumental variables to address the endogeneity problem. Specifically, we find that improving information disclosure ratings help firms attract more investor attention, which leads to higher investment efficiency. In addition, this information disclosure effect is more pronounced for underinvestment firms and firms on the main board than for smaller firms on SEM (small- and medium-sized enterprise) and GEM (growth enterprise market) boards. Our evidence supports the idea that regulatory activities for information disclosure ratings of companies listed on China’s stock exchanges improve investment efficiency.

Does the ancient farming culture shape the risk-taking behavior of households today? Using a dataset covering over 130,000 households from a Chinese national survey, our study examines the relationship between the culture of rice cultivation and the financial behavior of modern households. We find that households in regions with a higher rate of historical rice cultivation are more likely to invest in the financial market and buy lottery, but less likely to purchase insurance. We also find that the rice area has more households with risk preferences consistent with prospect theory expectations. To account for omitted variable bias, we use average regional rainfall and downstream distance to ancient irrigation systems as instrumental variables for rice cultivation, and our results remain robust. We find that the rice effect cannot be explained by regional economic development, traditional Confucian values, or ethnic diversity. To explore potential mechanisms, we find that households in rice regions are more likely to borrow money from friends and relatives and have interest waived, and historical commercial development has also been influenced by the rice culture.

Large technology firms have substantial advantages in data, a key non-rival input for developing AI technology. We argue that investments by large technology firms stimulate innovation by AI startups through the sharing of data, bringing more than money to the startups. We assemble a unique dataset containing (nearly) the universe of AI-inventing firms in China to examine the innovation effects of these investments. Our difference-in-differences estimation shows that, after receiving investments from large technology firms, AI startups increase the number of AI patent applications by 62% and the number of software products by 56%, relative to their mean values prior to the investments. Using a triple-differences strategy, we further find that the innovation impact of investments by large technology firms is stronger than that of investments by other firms without data advantages. We confirm these findings using an instrumental variables approach based on recent investments by large technology firms in peer startups. Finally, we provide novel evidence that the innovation effect works mainly through sharing non-rival data by leveraging our rich information on non-AI data-related patent applications and data-related online job postings.

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 paper explores the relationship between corporate social responsibility (CSR) and timeliness of goodwill impairment. Goodwill is the premium that is paid when a business is acquired. If the value of the business declines, goodwill impairment occurs. Deliberately delaying goodwill impairment (timeliness) is a widespread ethical issue. Based on all the mergers of Chinese listed companies during 2010–2019, we study the motivation of corporate charitable donations when facing the risk of goodwill impairment. Our results suggest that long-term (consistent) charitable donations reflect more altruist social responsibility than short-term (suddenly increased) donations. In particular, firms that make more long-term donations tend to report goodwill impairment timely, while firms making excessive short-term donations are more likely to delay goodwill impairment. Furthermore, we find that short-term donation is motivated not only to cover up the goodwill impairment delay, but also to provide insurance-like protection when delayed impairment is announced. Our results also suggest that moral licensing plays a role in inducing such opportunistic behaviors. To address the endogeneity problem, we use the number of provincial charitable funds and the number of provincial deaths due to natural disasters as instrumental variables for short-term excessive donations.

The inelastic markets hypothesis states that the aggregate stock market price elasticity of demand is small, implying that flows have large impacts on prices. We exploit demand shocks created as investor funds are frozen and unfrozen during Chinese IPOs to estimate the impact of demand shocks on the Chinese stock market. Using brokerage account records, we observe the selling and buying as investors raise cash to subscribe for IPOs and then reinvest the funds that supported unsuccessful subscriptions. Using an instrumental variables estimator we find that a 10 bps demand shock increases stock prices by between 30 and 48 bps.