This paper studies whether financial technology (FinTech) changes loan contract design by reducing banks’ reliance on collateral in corporate lending. Using loan-level data on Chinese listed firms from 2007 to 2023 and exploiting the People’s Bank of China’s 2019 FinTech Development Plan as a quasi-natural experiment, we find that banks with stronger pre-policy FinTech capability significantly reduce secured lending after the policy shock. In the benchmark specification, the probability that a loan is secured falls by 1.64 percentage points, or about 2.7% relative to the baseline secured-loan share. The result is robust to alternative loan classifications, matching procedures, alternative measures of FinTech adoption, aggregated lending outcomes, and alternative inference procedures. The pattern is more pronounced among small and medium-sized enterprises, lower-tier branches, and branches located outside bank headquarters’ cities, where borrower information is likely to be more limited. Supplementary analyses are consistent with FinTech reducing banks’ information-production costs and suggest that technological proximity to FinTech-active peers may amplify the collateral-reducing effect. Overall, the evidence indicates that FinTech can enhance banks’ screening capacity and shift lending decisions away from reliance on asset-based guarantees toward information-based credit assessment.

This paper examines the spillover effects of China’s Carbon Emissions Trading Scheme (CETS) on non-regulated firms’ bank loans. Using a sample of Chinese A-share listed firms and a staggered difference-in-differences design, we find that suppliers experience a significant decline in bank loans when their customers are included in the CETS. This effect is driven by reductions in firms’ cash flow and customer concentration. The negative effect of downstream CETS on suppliers’ bank loans is attenuated for suppliers with better environmental performance, more comprehensive carbon disclosure, and closer geographic proximity to customers. We also find that, in response to reduced bank credit, firms rely more heavily on trade credit. Overall, this study sheds new light on the unintended financial consequences of CETS policy on non-regulated firms.

Breaking away from being “large but not strong” and accelerating the internal “dual circulation” reform to integrate the manufacturing and service industries is a daunting challenge. This study examines how environmental regulations and financial instruments can simultaneously drive servitization evolution and green transformation. Utilizing the Green Credit Guidelines (GCG) policy rollout by China in 2012 as a quasi-natural experiment, we analyze 2007-2021 data from A-share listed manufacturing corporations through DID model to evaluate the policy ramifications and investigate servitization direction. The results show that: (1) While GCG generally promotes overall servitization, it biases firms toward traditional rather than modern servitization pathways. (2) Contrary to typical innovation compensation effects, GCG induces short-sight in managerial decisions, favoring quick wins over innovation-driven progress. These results highlight why firms have tended to advance traditional servitization while constraining modern servitization efforts. (3) Heterogeneity analysis shows stronger policy impacts in firms with domestically-oriented executives and domestic ownership, where both overall and traditional servitization are significantly enhanced.

Bankruptcy abuse prevention has been criticized for increasing foreclosure rates, imposing negative impacts on housing markets, and aggravating the financial crisis. By contrast, this paper documents that bankruptcy abuse prevention reduces household debt overhang, a phenomenon harmful to home values and housing markets. Using a difference-in-differences analysis, we find that households in recourse states increased their home improvement and maintenance expenditures after the Bankruptcy Abuse Prevention and Consumer Protection Act, a period during which the households paid considerable attention to the downside risk of the housing market, and that the effects vary by home equity level. The results remain unchanged with alternative specifications and cannot be explained by credit changes, judicial and nonjudicial foreclosures, homestead exemption, house sales, or heterogeneous expectations. Last but not least, we use entropy balancing to eliminate the differences between the treatment and control groups and get similar results. 预防破产滥用的政策被认为推高止赎率、对住房市场造成负面影响以及加剧金融危机而饱受批评。与之相反，本文证明破产滥用预防能够缓解家庭债务积压（debt overhang）—— 而债务积压恰恰是一种损害房屋价值与住房市场的现象。采用双重差分（DID）分析，我们发现：在《破产滥用预防与消费者保护法案》（BAPCPA）实施后，**追索权州（recourse states）**的家庭增加了住房改善与维护支出；该时期家庭对住房市场的下行风险高度关注，且上述效应因房屋净值水平的不同而存在异质性。在多种替代设定下结果依然稳健，且不能被信贷变化、司法与非司法止赎、宅基地豁免、房屋销售或异质性预期所解释。最后，我们使用熵平衡（entropy balancing）方法消除处理组与控制组之间的差异，同样得到了一致的结果。

Recent technological advancements have enabled the emergence of business organizations fully managed by algorithms, such as decentralized autonomous organizations (DAOs) or through artificial intelligence (AI), as observed in China’s online food delivery sector. These organizations are collectively referred to as algorithm-managed firms (AMFs). Given machines’ capabilities in data collection and analysis, human directors are increasingly being replaced by algorithms or AI in specific sectors. This article contends that algorithms can effectively take over human directors’ managerial, monitoring, and mediating roles. The diminishing role of human directors raises certain concerns of stakeholder protection. Unlike human directors, algorithm directors or managers would not consider stakeholders’ interests unless clearly instructed to do so. However, the algorithm supplier and the AMFs may lack the incentives to fully consider stakeholders because they do not always internalize the social costs. To address the challenges of AMFs, policymakers need to consider different regulation strategies. First, they must choose between command-and-control regulations and target-based regulations. Command-and-control regulations often do not work well because regulators lack enough information or control over complex algorithms. Instead of setting detailed technical rules, policymakers should adopt target-based regulations that let the algorithm balance various interests and regulate its own operations. Second, policymakers should decide between entity-based and algorithm-based regulations. Algorithm-based regulation is more suitable because it prevents companies from passing costs onto society. The state could consider regulating the composition of the board of directors of the algorithm supplier to ensure that they incorporate the concerns of stakeholders’ interests in the development of the algorithm. Additionally, corporate law doctrines that protect creditors and other stakeholders, such as piercing the corporate veil and limiting liability for corporate torts, must be revisited and modified because their foundational assumptions no longer align with the realities of AMFs.

Using proprietary data from China’s interbank bond-pledged repo market, we show that the interest-rate risk and credit risk of the pledged bond are key determinants of repo pricing. From a bond-option perspective, we develop arbitrage-free models that anchor the repo yield curve to the pledged-bond yield curve. The fair repo haircut is interpreted as the per-unit price of a call option on the pledged bond. We extend this framework to incorporate bail-in or bail-out potential, which enhances the model’s empirical performance and provides a novel explanation for systematic repo cheapness and existence of negative haircuts.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen ’ s capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.