Developing countries are taking advantage of FinTech tools to provide more people with convenient access to financial market investment through digital wealth management. Using COVID-19 as an uncertainty shock, we examine whether and how digital wealth management affects the resilience of consumption to shocks based on a unique micro dataset provided by a leading Big Tech platform, Alipay in China. We find that digital wealth management mitigates the response of consumption to uncertainty shocks: residents who participate in digital wealth management, especially in risky asset investments, have a lower reduction in consumption. Importantly, digital wealth management helps improve financial inclusion, with a more pronounced mitigation effect among residents with lower-level wealth, living in less developed areas, and those with lower-level conventional finance accessibility. The mitigation effect works through the wealth channel: those who allocate a larger proportion of risky assets in their portfolio and obtain a higher realized return show more resilience of consumption to negative shocks. We also find that digital wealth management substitutes for conventional bank credit but serves as a complement to FinTech credit in smoothing consumption during uncertainty shocks. Digital wealth management provides a crucial way to improve financial inclusion and the resilience of consumption to shocks.

Using bank-county-year level mortgage application data, we document that minority borrowers are systematically evaluated with less soft information compared to White borrowers within the same bank-county branch. Using variation in local sunshine as an instrument and conducting a series of robustness checks, we show that the soft information imbalance significantly increases the denial gap between minority and White applicants. However, this imbalance does not appear to affect pricing disparities. Further analysis shows that internal capital reallocation to under-resourced bank branches can serve as an effective strategy to reduce soft information imbalances and, thus, promote more equitable credit allocation. Our results highlight that soft information imbalance is an overlooked but significant factor driving disparities against minority borrowers.

This study examines the economic consequences of China’s Three-Red-Lines policy—introduced in 2021 to cap real estate developers’ leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance, with privately-owned developers disproportionately affected relative to state-owned firms. Using granular project-level data, we document significant drops in sales and a demand shift from private to state-owned developers. The policy also reduced local governments’ land sale revenues, prompting greater reliance on hidden local government financing vehicles for land purchases. The policy induced broad structural changes in China’s housing and land markets.

A unique natural experiment in China – the city-level staggered introduction of admin-istrative approval centers (AAC) – reduces bank loan processing times by substantially speeding up the process of registering collateral without affecting credit decisions. Fol-lowing the establishment of an AAC, firms significantly reduce their cash holdings. State-owned enterprises are less affected. Cash flow sensitivity of cash holdings de-creases, as does the cash flow sensitivity of investment. The share of short-term debt increases, while inventory holdings and reliance on trade credit decrease. Defaults also decrease. These results suggest that timely access to credit has important implications on firms’ financial management.

This paper revisits the default-crash risk relation in the context of China. We find that firms with higher default risk have lower stock price crash risk both in monthly and yearly frequencies. To identify the causal effect, we use the first-ever default event in China’s onshore bond market in 2014 as an exogenous shock to the strength of implicit guarantees. The negative relation arises from the active involvement of the government before 2014 and creditors after 2014 in corporate governance. Consistent with the external scrutiny mechanism, the impact of default risk on stock price crashes is stronger in situations in which creditors are more likely to engage in active monitoring (i.e., firms with higher liquidation costs, lower liquidation value, and higher levels of information asymmetry), with these effects primarily observed in the post-2014 period. Overall, our study highlights the role of the “invisible hand” in the absence of the “visible hand.”

Using data from Chinese A-share listed firms from 2015 to 2022, we employ a difference-in-differences model to empirically examine the impact of bankruptcy regimes, marked by the establishment of bankruptcy courts, on firms’ total factor productivity (TFP). The results show a significant decline in TFP among firms in regions following the establishment of bankruptcy courts. This finding remains valid after a series of robustness tests. Mechanism tests reveal that establishing bankruptcy courts increases firms’ risk aversion incentives by endowing creditors with excessive rights. Consequently, firms tend to reduce liabilities, curtail R&D investment, and accumulate liquid assets as coping measures, ultimately contributing to a decline in TFP. Furthermore, this effect is more pronounced for firms with high financial risk. However, the improvement of the market mechanism can alleviate the negative impact of bankruptcy courts excessively strengthening creditor protection. Specifically, when firms are located in regions with weak government intervention and strong financial development, as well as in market environments with low uncertainty and strong competition, this negative impact can be mitigated. These findings provide fresh insights into the dual nature of creditor protection and offer valuable references for governments to improve the bankruptcy legal system.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

Enterprise innovation is a hot topic in current academic research. Taking the demonstration city of social credit system construction implemented in China as a quasi-natural experiment, this paper investigates whether the construction of social credit system can improve enterprise innovation. The study finds that the construction of social credit system effectively enhances enterprise innovation. Mechanism test shows that the construction of social credit system escalates the scale and duration of enterprise loans, thereby fostering enterprise innovation. These findings present insights that the pivotal role of informal institutions, such as the social credit system, in facilitating the upgrading of industrial structures and augmenting the quality of economic development.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

This study assesses the impact of China’s Green Credit Guidelines (GCG) 2012 on the quality of firms’ green innovation and their financial asset allocations. While examining patent applications and grants, our findings reveal that, although the GCG 2012 led to a significant increase in green patent applications, its influence on granted patents, especially in the invention category, was minimal. This highlights a discrepancy between innovation intent and quality, suggesting that highpolluting enterprises (HPEs) prioritize rapid policy compliance rather than substantial environmental improvements. However, HPEs seem to prioritize liquidity over long-term financialization, potentially indicating enhanced credit allocation efficiency.