The ongoing buildup of local government hidden debt not only ramps up fiscal and financial risks but also affects the financing environment for individual firms by squeezing credit resources and distorting interest rates. This paper constructs a three-stage sequential game model including local governments, commercial banks, and non-financial firms, analyzing the issue from two angles: “policy effect” (whether to swap debt) and “scale effect” (how much to swap), explaining theoretically how local government debt swaps impact corporate default risk. The model shows that, through the policy signal channel, debt swaps exogenously reduce local governments’ default probabilities, lower regional financial risk premiums, and improve expectations for bank asset quality. At the same time, the scale of swaps changes bank balance sheets, creating two opposing forces: “repair of the balance sheet” and “compression of asset returns”. These forces dynamically evolve with the swap scale and jointly determine the ultimate risk effect of the policy. Based on this, using data from non-financial firms listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2023, this paper treats the 2015 local government debt swap as a quasi-natural experiment and applies a multi-period difference-in-differences (DID) approach for empirical testing.
Empirical research has found that local government debt swaps significantly reduce corporate debt default risk. This conclusion holds even after parallel trend tests, PSM-DID tests, variable replacements, instrumental variable methods, and placebo tests. Mechanism analysis indicates that debt swaps mainly work through two channels: lowering regional financial risk premiums and improving bank asset quality. In areas with high local debt risk, weak corporate repayment capacity, and low bank risk-taking, the policy’s risk mitigation effect is more pronounced. Heterogeneity analysis shows that non-state-owned enterprises, small and medium-sized enterprises, high leverage and high gold melting enterprises benefit more significantly due to their tighter financing constraints. After replacing the pressure drop risk premium, the marginal financing cost decreases more, and the relief effect is prominent; Regions with high marketization and high fiscal capacity, due to sufficient financial competition, solid government credit, smoother credit restructuring, and stronger risk mitigation effects of trade in policies on their affiliated enterprises. Further research finds a robust U-shaped relationship between swap scale and corporate default risk: moderate swaps mitigate risk by expanding bank credit volume, but beyond a certain threshold, low-interest swap bonds continuously squeeze bank asset yields, leading to increased credit pricing and reduced supply, with the marginal risk mitigation effect declining or even reversing. The interaction of swap scale with bank credit volume and pricing is significantly positive, confirming that the scale effect is transmitted through both credit “quantity” and “price” channels as expected.
This paper puts forward the following policy recommendations. First, fully recognize the positive role of debt swaps in mitigating systemic financial risks and maintain the continuity and stability of debt-reduction policies. Second, scientifically estimate the optimal range of swap volumes for each province and reasonably plan the issuance pace to avoid short-term concentrated swaps putting pressure on bank credit supply. Third, remove key bottlenecks in policy transmission and guide commercial banks to allocate the released credit resources effectively to the real economy, especially favoring non-state-owned enterprises and highly leveraged companies that benefit more. Fourth, implement differentiated regional debt strategies, pairing fiscally weaker areas with central transfer payments and provincial coordination measures to strengthen the credit basis for effective policy transmission. This paper provides an important reference for decision-making in optimizing debt-reduction policy design and achieving both risk mitigation and resource allocation optimization.

Government investment funds in China serve as both a key vehicle of the sci-tech finance system and an increasingly important instrument for local governments to implement industrial policies. Governments at various levels contribute fiscal capital as limited partners, channeling private investment into strategic emerging industries and critical sectors, with the stated objectives of fostering new quality productive forces, supporting technological innovation, and promoting local industrial upgrading. Unlike venture capital and private equity models dominated by private capital in Western countries, China's government investment funds are mostly funded by local fiscal budgets and subject to local performance assessment, making their investment decisions heavily reflective of local industrial policy orientations. By 2025, over 2,100 government investment funds had been established nationwide, with a target scale of approximately RMB 11 trillion, and sub-provincial governments had become the main sponsors. Over the past two decades, local industrial policies have exhibited pronounced homogenization: provinces and cities compete to prioritize the same sectors, including new energy and intelligent connected vehicles, advanced manufacturing, new materials, biomedicine, and big data. This gives rise to the central question of this study: how does horizontal intergovernmental competition, triggered by industrial policy homogenization, affect the spatial allocation of fiscal equity capital?
This study proposes that industrial policy competition among local governments is a key constraint on the cross-regional allocation of fiscal equity capital, and explains this through two mechanisms. The first is economic rationality: industrial policy homogenization reduces the differentiated institutional rents available from out-of-region investments, homogenizes the project pool, raises screening costs, and reinforces expectations of overcapacity, thereby lowering the net returns on cross-regional investments. The second is intergovernmental competition: because the funding and performance evaluation of government investment funds are embedded in local agendas, when cities within a province compete in similar sectors, local officials have incentives to retain fiscal equity capital locally, and canimpose constraints on fund managers through seats on investment committees, performance appraisals, and commitment to future follow-on funding.
Accurately measuring the degree of industrial policy homogenization is a core challenge. This study leverages a large language model to overcome the limitations of keyword-based searches and subsidy proxies: we apply DeepSeek-V3, an open-source model developed domestically, to process 1.77 million policy documents from the PKULAW database (2011–2022). The model identifies industrial policy documents based on four criteria, including government agency, specific instruments, target industries, and long-term objectives, and extracts key industries, policy goals, and instruments by “quoting original texts” to mitigate hallucination. We then construct city-year policy vectors and compute cosine similarity between each city and other cities within the same province. This measure is matched with 20,836 government fund investment events identified through multi-layer equity penetration and manual verification, classified into local investments (11,383) and cross-regional investments (9,453), forming a panel dataset covering 300 prefecture-level jurisdictions and 3,350 city-year observations. Fund investment data are obtained from Qingke PEdata, and city-level fundamentals and additional test data are drawn from various statistical yearbooks, CNRDS, CnOpenData, and other sources with cross-validation.
Baseline regressions show that the more similar a city's industrial policy is to those of other cities in the same province, the significantly less cross-regional government fund investment its local firms receive. This result survives a series of robustness checks. Mechanism tests indicate that the negative effect stems from both economic rationality and intergovernmental competition. The intergovernmental competition effect is evidenced by: (1) in the first year of a new Party Secretary's tenure, the marginal effect of policy similarity on cross-regional investment is about 2.2 times that in other periods; (2) the baseline effect is driven solely by policy similarity with intra-provincial cities, not by similarity with extra-provincial cities. Heterogeneity analyses show that the effect is stronger in cities with tighter fiscal constraints, concentrated in industries prioritized by central and provincial governments, and primarily driven by homogenization in supportive policy instruments. Analysis of economic consequences finds that industrial policy homogenization significantly weakens the positive effects of government investment funds on firm entry and intellectual property transactions.
This study offers three policy recommendations. First, for strategic emerging and future industries with strong economies of scale, the central government should take the lead in coordinating major investment decisions and give national-level funds a stronger role in crossregional allocation. Second, local industrial policies should move away from imitation toward a more differentiated approach grounded in comparative advantage. Evaluations of official performance should give due weight to fostering locally distinctive industries that fit regional conditions, and a mechanism for crossregional policy coordination and information sharing should be developed within the unified national market framework. Third, transfer payments and supporting fund mechanisms need to be improved to bolster policy implementation in fiscally weaker areas. At the same time, information disclosure and accountability should be enhanced to create a more favorable institutional environment for the crossregional flow of fiscal equity capital.

This paper examines how bank capital regulation affects local government financing costs through commercial banks’ incentives in the primary bond market. The setting is China’s local government bond market, where commercial banks are the dominant investors and underwriters, and where the design of regulatory capital rules can directly influence the demand for government bonds. In November 2023, China announced Rules on Capital Management of Commercial Banks, which lowered the risk weight on banks’ holdings of local government general bonds from 20 percent to 10 percent while keeping the risk weight on special bonds unchanged. This reform corrected a regulatory mismatch: general bonds are mainly supported by local general public budget revenues and generally carry lower repayment risk, whereas special bonds rely more on government-fund revenues or project-related cash flows, yet the two had previously received the same capital treatment. The reform therefore provides a useful quasi-natural experiment for studying whether a more risk-sensitive capital rule can reduce explicit local government financing costs through market incentives rather than direct fiscal subsidies or administrative intervention. The paper develops a bank bidding model under China’s Dutch auction issuance system to show that lowering the risk weight of general bonds reduces the capital cost of these bonds, raises banks’ willingness to bid for them, and improves the market’s ability to absorb new issuance. The model predicts that the financing-cost reduction should be stronger when bank underwriters face tighter capital constraints and when the recent supply of local government bonds is larger. Using transaction-level issuance data on local government bonds from January 2021 to June 2025, drawn from Wind, iFinD, East Money Choice, and DM database, the paper employs a difference-in-differences framework to compare refinancing general bonds with refinancing special bonds.
These two types of bonds have similarrefinancing purposes and comparable issuance mechanisms, but only general bonds are affected by the risk-weight reduction. The results show that the capital reform significantly lowers the issuance spreads of general bonds over comparable treasury bond yields. The finding is robust to a series of tests, including checks for pre-trends, placebo reforms, alternative sample windows, controls for other fiscal policies, alternative fixed effects, and additional measures of primary-market bidding conditions. Mechanism tests support the model: the effect is stronger for bonds underwritten by banks with lower capital adequacy ratios, and is stronger when the market faces greater recent issuance pressure, indicating that the reform works by relaxing bank capital constraints and improving market absorption capacity. Further analysis suggests that the reform also improves the transmission of market benchmark rates to local government bond pricing and supports credit expansion among more capital-constrained banks, especially in regions with larger stocks of general bonds. These findings imply that well-designed capital regulation can lower local government borrowing costs without weakening market discipline or increasing direct fiscal incentives.
The paper contributes to the literature in three ways.First, it introduces a financial-regulatory perspective to the study of local government bond pricing, while existing literature has mostly focused on fiscal fundamentals, government behavior, information disclosure, default risk, and tax policy. Second, it extends the literature on bank capital regulation by showing that risk-weight adjustments affect not only bank stability and credit supply, but also government bond pricing at the micro level. Third, it also provides evidence on fiscal-financial coordination by showing that regulatory design can help establish a long-term government debt management mechanism. The policy implication is that regulators should continue to refine risk-weight design according to the actual risk characteristics and policy functions of different bond types, while complementing capital regulation with market-based tools that reduce banks’ overall cost of holding local government bonds. Future research can examine the longer-run effects of the reform, its impact on secondary-market liquidity and investor structure, and the interaction between risk-weight policy, monetary policy, and macroprudential regulation.

Tariff variations exert direct effects on global commodity flows, industrial configurations, and worldwide economic welfare, ultimately shaping the division of labor and the distribution of gains in international trade. This paper investigates the impact of a country's tariff policy on both the level and position of Global Value Chain (GVC) participation for itself and its trading partners, and assesses the ramifications of this trade barrier for global production patterns. We first develop a partial equilibrium model of an open economy to examine the effects of tariffs on GVC participation intensity and positioning. In the theoretical framework, tariffs operate as a price markup that shapes micro-level firms' import-export decisions.
This paper delineates three channels through which import tariffs affect GVC engagement.(1) Intermediate input structure. Tariff hikes alter the relative prices of intermediate goods between the home country and its trading partner, inducing adjustments in input composition via relative demand substitution. In response, firms re-optimize their sourcing strategies by seeking alternative global suppliers or relocating production stages domestically to hedge against risks, thereby diminishing both the intensity and positioning of GVC participation. (2) Factor input structure. Tariff increases raise the relative user cost of tradable capital inputs while simultaneously lowering the relative cost of labor inputs. This relative price shift prompts firms to reallocate production towards more labor-intensive and lower value-added stages, consequently reducing both the intensity and positioning of GVC participation. (3) Technology gap between trading partners. Higher tariffs escalate the costs associated with GVC participation, which crowds out R&D expenditure, curtails technology spillovers embodied in imports, and attenuates innovation incentives. The resultant widening of the technology gap, both between the trading partners and vis-à-vis the global technological frontier, further depresses the intensity and positioning of GVC participation.
The main findings of this paper are threefold. First, an increase in import tariff rates reduces the intensity and positioning of GVC participation for both trading parties through the aforementioned channels, including the intermediate input structure, the factor input structure, and the technology gap. Second, the impact of import tariff hikes exhibits marked asymmetry between the home country and its trading partner. When the home country is relatively disadvantaged vis-à-vis its trading partner in terms of GVC participation intensity, GVC positioning, level of economic development, trade infrastructure, and labor endowment, raising its import tariffs inflicts a disproportionately larger negative effect on its own GVC engagement, which is a phenomenon akin to proactively “hitting a stone with an egg.” Third, empirical evidence corroborates the theoretical predictions and further reveals that a 10-percentage-point increase in a country's import tariff rate reduces the GVC positioning of the home country and its trading partner by 3.82 and 2.67 positions, respectively.
The marginal contribution of this paper is twofold. On the one hand, we identify and clarify the mechanisms through which tariff policy affects the intensity and positioning of GVC participation, while systematically comparing the differential effects of tariff policy on the two trading parties. On the other hand, whereas the extant literature predominantly concentrates on the domestic repercussions of tariff policy, this study further demonstrates that, conditional on the economic power asymmetry between the two parties in tariff competition, tariff changes of equivalent magnitude generate heterogeneous policy outcomes across trading partners. This offers novel analytical perspectives and empirical evidence for deciphering the mechanisms and magnitudes through which tariff policy shapes GVC production in both the home country and its trading partner. Moreover, our investigation sheds light on the underlying rationale for countries' adoption of discriminatory trade policies and serves as a cautionary note that a country should more rigorously evaluate and anticipate the policy costs and relative gains of protectionist trade measures from perspectives of both the domestic structure and international trade.

China’s rapid urbanization has been accompanied by a substantial increase in migrant population. In the context of the country’s people-centered new urbanization strategy, which aims to integrate migrants into urban life and ensure equitable access to public services, understanding how migrant households invest in their children’s education has become critical. Education is widely viewed as a primary pathway for social integration and intergenerational mobility. However,migrant households face distinct challenges, including income volatility, limited public service access, and weakened social networks, which constrain their resource allocation decisions. This study systematically investigates three questions: whether migrant households invest more in education than local families, what drives these investment patterns, and how these investments affect children’s developmental outcomes.
Using data from the 2017 and 2019 waves of the China Household Finance Survey,we find that migrant households allocate significantly more resources to education than local households, with educational expenditure 14.54 percent higher and its share in total household spending 0.68 percentage points higher, a pattern that remains robust across multiple specifications and datasets. This higher investment is accompanied by increased savings and reduced spending on discretionary consumption, indicating a reallocation toward long-term human capital formation. Mechanism analysis shows that the higher educational investment by migrant households cannot be fully explained by old-age security motives, risk-sharing considerations, or disparities in access to public educational resources. Instead, the findings suggest that concerns about children's long-term development and intergenerational mobility play an important role. First, migrant households experienced significant upward social mobility, and this experience may strengthen their belief in education as an effective pathway for success, leading to substantially higher investment. Second, the relative educational investment of migrant households, compared with that of local households, is more pronounced when they perceive their living standards to be lower than those of their relatives or neighbors. Similarly, the relative educational investment of migrant households is more pronounced in cities with limited public educational resources and among families with weaker social networks.
Despite this higher investment, the study finds that, on average, migrant children do not achieve greater upward intergenerational mobility in terms of educational attainment or income compared to their local peers. This null result masks significant heterogeneity depending on the destination city’s opportunity structure. In cities with high intergenerational mobility, migrant children achieve modestly more schooling and higher income than local children, suggesting that a more favorable opportunity structure may facilitate the conversion of family educational investment into better outcomes for migrant children. This finding demonstrates that the returns to educational investment are highly contingent on the institutional context; in cities with limited opportunities for upward mobility, greater family educational investment may not be sufficient to overcome the constraints faced by migrant children.
This study makes three key contributions. First, it systematically analyzes the motivations and outcomes of educational investment among migrant households in China, revealing the long-term effectiveness of such investment and its critical dependence on socio-environmental factors, thereby offering important theoretical insights and empirical support for understanding the complex relationship between migrant families’ educational behavior and intergenerational mobility. Second, it identifies key mechanisms that significantly influence migrant households’ investment decisions, including beliefs about the returns to education shaped by past upward mobility, social comparison with relatives and neighbors, and external support in the form of public educational resources and informal social networks, adding to the literature on household financial decision-making and educational investment while providing a novel explanation for the drivers of migrant educational investment. Third, by focusing on the process of people-centered urbanization, it examines how migrant households adjust resource allocation to cope with socioeconomic challenges in destination cities, extending existing research on migration and educational investment and offering policy insights for facilitating migrant integration and achieving sustainable urban development.

Regional coordinated development is a strategic priority in China. Cross-regional capital flows and allocation directly affect regional economic disparities and are a key factor in promoting regional coordinated development. Compared with developed regions, the degree of capital agglomeration in China’s underdeveloped regions remains relatively low. Achieving the allocation of resources across geographical regions is one of the fundamental functions of the financial system. With the deepening application of technologies such as artificial intelligence, big data, blockchain, and cloud computing, China’s financial system has undergone rapid innovation and has become an important force in enhancing the role of the market mechanism. China’s financial system relies heavily on the banking sector, which makes banking financial service innovation particularly noteworthy. Does banking financial service innovation affect cross-regional capital flows? What is the mechanism? Can it guide capital flows toward underdeveloped regions? Addressing these questions clarifies the theoretical channels of how finance promotes regional coordinated development and offers important insights for boosting capital agglomeration in underdeveloped regions and optimizing the policy system.
This paper constructs a two-region theoretical model of capital flows to explore the impact of banking financial service innovation on cross-regional capital flows. On this basis, it constructs an index of banking financial service innovation using the number of news reports, measures capital flows employing the full set of business registration data of Chinese enterprises, and constructs city-pair-level data from 2011 to 2023 for empirical testing. The results show that, first, banking financial service innovation promotes cross-regional capital flows, and this conclusion remains valid after a series of robustness checks. Second, banking financial service innovation promotes capital flows by improving the spatial accessibility of financial services and reducing the frictional cost of enterprises’ cross-regional investment. The promotional effect is more significant when the capital-outflow region has strong innovation capacity and advanced digital adoption capability, and when the capital-inflow region features a high degree of marketization and a low level of financial risks. Third, banking financial service innovation encourages capital to flow to regions with high production efficiency and high returns on capital, and steers capital from developed regions to underdeveloped regions, thereby helping to narrow the interregional capital allocation gap. Fourth, within provinces and urban agglomerations, the role of banking financial service innovation in driving cross-regional capital flows is more prominent, while for the three major urban clusters, it exhibits a stronger cross-megaregion capital flow effect.
This paper proposes the following policy recommendations. First, deepen banking financial service innovation and enhance the role of the market mechanism in capital allocation based on local conditions. Second, consolidate the foundations for the role of banking financial service innovation, and facilitate the two mechanisms of improving spatial accessibility and reducing frictional cost. Third, strengthen efficiency orientation and coordinated linkages, and empower regional coordinated development through banking financial service innovation.
The marginal contributions of this paper are as follows. First, it establishes an analytical framework for the impact of banking financial service innovation on cross-regional capital flows, enriches the literature on capital flows from a financial perspective, and lays a theoretical foundation for promoting regional capital flows and optimizing allocation. Second, proceeding from the service functions of the banking sector, it categorizes such innovation into traditional financial service innovation and non-financial value-added service innovation, systematically analyzes the mechanism through which banking financial service innovation affects capital flows, and provides empirical evidence for promoting cross-regional capital flows through market-oriented approaches. Third, it examines the impact of banking financial service innovation on capital flow efficiency, spatial boundaries, and flow directions between regions with different levels of development, offering new evidence for resolving theoretical debates on capital flows and advancing regional coordinated development.

In the context of sluggish global economic growth, the Chinese economy is facing increasing downward pressure, accompanied by weak corporate investment incentives, and slow fixed-asset investment growth. Revitalizing corporate investment requires not only sound policy arrangements that encourage firms to invest but also an effective financial system that enables them to obtain sufficient credit. In this context, improving banks' long-term lending mechanisms is essential for supporting investment in the real economy and promoting high-quality economic growth.
The rapid application of big data, cloud computing, artificial intelligence, and other digital technologies has made financial technology (FinTech) one of the most influential financial innovations. Within China's bank-dominated financial system, commercial banks have increasingly embedded FinTech into credit services, fundamentally reshaping lending decisions and risk management. Existing studies mainly examine how regional FinTech development affects corporate investment or focus on the financing function of bank FinTech. However, bank lending performs not only a financing function but also a monitoring function. Moreover, enhanced algorithmic risk assessment may unintentionally reduce credit accessto productive but risky firms. Whether bank FinTech innovation jointly affects corporate investment through credit financing, credit monitoring, and credit allocation remains largely unexplored.
Drawing upon financial intermediation theory, creditor monitoring theory, and investment substitution theory, this paper develops a unified analytical framework linking bank FinTech innovation to both the quantity and quality of corporate investment. We construct a bank-firm matched dataset based on the Bank Loan Details of Listed Companies from the Chinese Research Data Services Platform (CNRDS) from 2013 to 2023. Bank FinTech innovation is measured using a patent-based indicator identified through multiple cross-validation methods, including a FinTech dictionary, Latent Dirichlet Allocation (LDA) topic modeling, supervised machine learning, IPC classification, and manual verification. These data are merged with firm-level information from the China Stock Market & Accounting Research (CSMAR) database to examine the effects of bank FinTech innovation on corporate investment.
The empirical results reveal three main findings. First, bank FinTech innovation significantly increases corporate investment by improving credit accessibility and extending loan maturity. Additionally, strengthened credit monitoring reduces excessive cash holdings financed by bank loans and discourages firms from diverting credit funds into financial asset investment, thereby mitigating financialization and promoting real investment. Second, although bank FinTech innovation alleviates inefficient underinvestment among underinvesting firms through improved financing, it does not significantly enhance overall investment efficiency through stronger monitoring. Third, further analyses from the perspective of credit allocation show that FinTech-enabled precision risk control reallocates credit away from high-risk but highly productive firms toward low-risk but less productive firms. This allocation pattern weakens banks' incentives to conduct effective post-lending monitoring and limits the flow of credit resources to more productive firms, thereby constraining the positive effect of bank FinTech innovation on investment efficiency. These findings suggest that, to further harness the potential of FinTech in supporting the real economy and promoting high-quality financial development, it is essential to optimize FinTech-enabled credit monitoring and precision risk management mechanisms. Such improvements would facilitate both the expansion of investment and the enhancement of investment quality in the real economy.
The academic contributions of this paper are threefold. First, unlike previous studies that rely on regional FinTech indicators or text-based measures, we develop a patent-based measure of bank FinTech innovation using multiple cross-validation methods and identify its asymmetric effects on the quantity and quality of corporate investment at the bank-firm level. Second, we integrate the financing and monitoring functions of bank credit into a unified framework, providing new evidence on the micro-level transmission mechanisms through which bank FinTech innovation affects investment in the real economy. Third, by uncovering why bank FinTech promotes investment expansion but fails to improve investment efficiency, this study offers new insights into optimizing credit allocation under FinTech-enabled banking.
Future research may extend the analysis to non-listed small and medium-sized enterprises and explore the heterogeneous effects of bank FinTech innovation across different financial regulatory regimes and market structures through cross-country comparisons.

Financial technology, as a core engine driving modern financial system, plays a pivotal role in deepening financial supply-side structural reform and fostering high-quality economic development. Since the 2008 global financial crisis, China’s macro leverage has risen persistently, and the COVID-19 pandemic has exposed firms’ debt vulnerabilities, making capital structure optimization a critical issue. In response, China has adopted the strategic principle of “coordinating development and security”, pursuing growth while building robust safeguards against systemic risks, and has mandated systematic digital transformation of financial institutions, shifting risk management from human-reliant prevention to technology-enabled intelligent control. As a key link, bank digitalization is profoundly reshaping credit allocation, risk identification, and bank-firm interactions, creating new possibilities for dynamic capital structure adjustment. Leveraging this transformation to accelerate firms’ adjustment toward target capital structure thus becomes central to preventing major financial risks and advancing high-quality development. However, while extensive studies examine traditional bank factors, including credit supply, bank connections, and credit market frictions, the role of banks’ own digital transformation remains largely unexplored.
To address this gap, this paper takes A-share listed companies in China from 2014 to 2023 as the research sample and empirically investigates the impact of commercial bank digital transformation on the dynamic adjustment of corporate capital structure through the lens of bank-firm relationships. The findings reveal that bank digital transformation significantly accelerates the speed of capital structure adjustment, a result that holds under a battery of robustness checks. Regarding the sub-dimensions of digital transformation, digital organization, digital products, and digital technology, all significantly enhance the speed of corporate capital structure adjustment. Mechanism analysis demonstrates two channels: first, bank digital transformation enhances banks’ information screening capability, enabling them to more accurately identify firms’ actual production, operational conditions, and credit quality; second, it strengthens banks’ monitoring and governance over borrowing firms, thereby alleviating agency problems and raising the speed of adjustment. Heterogeneity analysis indicates that the accelerating effect is more pronounced when the firm’s largest lending bank is a small or medium-sized institution, and during periods of elevated economic policy uncertainty. Further analysis shows that bank digital transformation helps mitigate credit discrimination embedded in traditional lending practices, specifically size-based discrimination and ownership-based discrimination, thereby promoting a structural adjustment of corporate leverage.
This study makes three main contributions. First, it integrates bank digital transformation into a unified bank-firm framework of capital structure dynamics, extending research on adjustment speed determinants. Prior studies focus on firm-internal and external factors; the limited fintech literature addresses only external digital finance for long-tail clients, overlooking banks’ internal digital transformation serving traditional corporate borrowers. Second, by uncovering the information screening and monitoring channels, this paper deepens the understanding of digital bank-firm interactions and enriches research on the economic consequences of bank digital transformation from the novel perspective of capital structure dynamics. Third, from the perspective of coordinating development and security, we show that bank digital transformation mitigates size- and ownership-based credit discrimination by accelerating the deleveraging of large and state-owned firms, offering clear references for financial supply-side structural reform, strengthening the real-economy service capacity of finance, and preventing financial risks.
Based on the above findings, this paper offers the following policy recommendations. First, continuously deepen bank digital transformation across all dimensions, harnessing its role in mitigating credit discrimination and optimizing capital structure. Second, embed digital tools into the full credit process, integrating transaction flows, tax records, and supply chain data with knowledge graphs and anomaly detection to build fund-flow monitoring systems, thereby reducing adjustment costs and empowering optimization through screening and monitoring. Third, implement differentiated, forward-looking policy guidance to amplify positive effects in critical periods and key areas. During heightened volatility, guide banks to strengthen dynamic credit management and risk early warning with digital tools; simultaneously enhance support for digital capacity building of small and medium-sized banks, improving their ability to identify financing needs and streamline credit processes, thus accelerating capital structure adjustment more effectively.

Enhancing supply chain efficiency has become a strategic imperative for strengthening China's global economic competitiveness and ensuring economic security in the context of external shocks and domestic structural transformation. While existing literature predominantly examines FinTech from the supply side, emphasizing how banks and FinTech firms reshape credit allocation, this study shifts to the corporate demand side and investigates whether proactive corporate FinTech investment influences supply chain efficiency. We posit a novel mechanism that moves beyond the conventional financing-constraints narrative: Fin Tech investment enables firms to shift from passive relief to proactive optimization of their internal credit architecture. We further extend the analysis to the network level, exploring the synergistic interaction between Fin Tech investment and supply chain finance.
Using a hand-collected dataset of FinTech-related job postings by Chinese listed firms from 2010 to 2022, we construct a firm-level measure of substantive FinTech investment. This metric captures genuine resource commitment, by treating hiring talent as the critical enabler of technological adoption, thereby avoiding measurement errors and “free-rider” issues inherent in aggregate indices or patent proxies. OLS estimates with firm and year fixed effects yield strong evidence that FinTech investment significantly enhances supply chain efficiency. This result is robust to a battery of robustness checks, including instrumental variable estimation, alternative specifications, and different estimators, leaving the baseline findings unchanged.
In our mechanism analysis, we identify a fourfold pathway through which FinTech investment systematically optimizes the firm's credit system: (1) increasing credit availability by mitigating information asymmetries and expanding financing channels; (2) enhancing credit flexibility by accelerating the adjustment of capital structure and refining the choice of credit maturities; (3)reducing direct and indirect credit costs by strengthening market competition and lowering risk premiums; and (4) mitigating credit risk through real-time monitoring and predictive analytics that lower default probabilities. Collectively, these four channels constitute a systematic upgrade from passive borrowing to active credit architecture management, which significantly extends the conventional unidimensional narrative of financing constraints.
Crucially, heterogeneity analysis reveals that the positive effect is significantly more pronounced among smaller firms, growth-stage enterprises, those in highly competitive industries, and firms located in cities with lower traditional financial institution density and more favorable business environments. This pattern underscores the inclusive, equalizing nature of FinTech-driven efficiency gains, suggesting it serves as a compensatory mechanism in regions underserved by conventional banking while thriving in institutional contexts marked by sound rule of law.
Extending our analysis to the supply chain network, we find evidence of spillover effects. The internal credit optimization generated by FinTech investment amplifies the effectiveness of supply chain finance. Core firms with advanced FinTech capabilities can build shared digital infrastructures such as dynamic discounting platforms and blockchain-based receivable confirmation systems. These infrastructures integrate transaction, logistics, and fund flows across tiers, enhancing the operating efficiency of all participating entities, particularly upstream SMEs facing themost severe financing frictions. This reveals a critical value-expansion trajectory: from serving the internal firm to reshaping the external ecosystem.
This paper makes three primary contributions. First, it introduces a demand-side perspective on FinTech, documenting how firms actively harness technology to transform financial operations, complementing the vast supply-side literature. Our job-posting-based metric offers a precise and forward-looking proxy for real FinTech activity. Second, it extends FinTech research into supply chain management, demonstrating that technology's benefits transcend individual firm performance to enhance inter-organizational coordination and network resilience. Third, theoretically, it introduces a dynamic “credit system optimization” framework, replacing the static “financing constraints” approach, and articulates a clear internal-to-external value diffusion mechanism with four distinct transmission channels.
Our findings carry significant policy implications. They advocate for policies encouraging corporate technology upgrading through tax incentives, digital infrastructure development supporting inter-firm data sharing, and regulatory frameworks fostering synergies between FinTech and supply chain finance, especially in underserved regions.

The blockade and cutoff of core technologies have become a defining feature of the intensifying rivalry among major economies. Since it first sanctioned ZTE in 2018, the United States has progressively escalated its export controls against China, shifting from sanctions on individual firms toward a comprehensive encirclement of critical-technology supply chains. The representative instrument in this shift is the Entity List administered by the U.S. Bureau of Industry and Security (BIS). By the end of 2024, nearly one thousand Chinese entities had been listed, marking a shift in U.S. export controls from targeted restrictions to a de facto technology cutoff. Given rising risks of decoupling and supply-chain fragmentation, understanding how technology cutoffs affect Chinese firms' efforts to extend and strengthen domestic supply chains through innovation, and revealing the mechanisms through which supply-chain spillovers operate under external shocks,are of both theoretical and practical importance, particularly for China's pursuit of high-level science-and-technology self-reliance.
In this paper, we address two questions. First, does a technology cutoff strengthen or weaken the innovation of the firms it directly targets? Second, and more importantly, does the innovation response of targeted firms spill over through supply-chain linkages and promote innovation among their downstream firms? From a theoretical perspective, technology cutoffs may generate two opposing effects. On the one hand, the loss of access to key technologies and critical components may weaken firms' R&D capabilities and production efficiency, thereby constraining innovation investment and output (the constraint effect). On the other hand, external restrictions may induce firms to accelerate indigenous innovation, search for alternative technologies, and pursue technological breakthroughs (the forcing effect). Existing studies have not reached a consensus regarding which effect dominates. More importantly, little micro-level evidence is available on whether technology cutoffs generate cross-firm innovation spillovers through supply-chain relationships, particularly positive spillovers driven by firms' indigenous innovation.
We take the Entity List as a quasi-natural experiment and study a panel of Chinese A-share listed firms from 2016 to 2022. To ensure clean identification, downstream firms are defined by sales relationships established before 2018,so that the supply-chain structure is predetermined with respect to the technology cutoff, thereby avoiding endogeneity arising from changes in buyer-supplier relationships after the shock. We focus on downstream rather than upstream firms because, in critical-technology sectors, a targeted firm's upstream suppliers are often included on the Entity List, making upstream spillovers difficult to identify cleanly, whereas downstream transmission provides a clearer empirical setting.
The main findings are as follows. First, technology cutoffs significantly increase innovation by targeted firms, and the positive effect is concentrated in the technological fields directly affected by the cutoff, reflecting both the forcing effect and firms' efforts to strengthen domestic supply chains through indigenous innovation. Second, technology cutoffs generate positive supply-chain spillovers: innovation output increases significantly among downstream firms connected to targeted firms. Third, innovation spillovers are transmitted through three channels: market leadership, technology transfer, and knowledge sharing, thereby enhancing the innovation capability of downstream firms. Heterogeneity analysis shows that these downstream spillover effects are stronger when targeted firms receive greater government subsidies, are certified high-tech enterprises, possess higher technological autonomy, or maintain more stable supply-chain relationships.
The contributions of this paper are threefold. First, it shifts the focus from the “point” to the “chain”, showing that technology cutoffs not only induce indigenous innovation of targeted firms, but also generate positive spillovers to connected firms. This provides empirical evidence supporting China's strategy of strengthening and extending domestic supply chains to achieve technological self-reliance. Second, whereas previous supply-chain studies have primarily emphasized the negative contagion effects of shocks such as firm failures, natural disasters, and cyberattacks, this paper identifies a positive externality transmitted through innovation, thereby enriching the literature on supply-chain resilience. Third, while existing studies primarily examine backward innovation spillovers from downstream customers to upstream suppliers, this paper documents forward spillovers originating from strategically positioned targeted firms and identifies three empirically testable mechanisms.
Overall, the findings provide useful policy implications for promoting collaborative innovation across different stages of the supply chain, facilitating the diffusion of innovation outcomes, and designing targeted innovation policies for different types of firms.

The high-quality development of the credit rating industry is an important safeguard for the efficient allocation of resources in the bond market. As China's rating industry transitions from a regulation-driven regime to a market-driven one, obtaining issuer ratings from multiple credit rating agencies simultaneously has become increasingly common, with nearly half of Chinese bond issuers rated by more than one agency. On the one hand, ratings from different agencies may provide cross-validation and generate information gains. On the other hand, maintaining rating relationships with multiple agencies may intensify competition among rating agencies and distort the information embedded in ratings. Whether multiple issuer ratings improve or impair bond rating quality is therefore an open empirical question with direct policy relevance.
This paper first develops a game-theoretic model of the interaction between a bond issuer and credit rating agencies by extending the framework of Bolton et al. (2012). Under the issuer-pays model, an agency receiving an unfavorable private signal must weigh the business revenue from retaining the client against the expected reputational loss from issuing a distorted rating. Multiple issuer ratings make the issuer's threat of switching to another incumbent agency more credible and raise the agency's expected loss from losing the client relationship, thereby strengthening its catering incentive. The model predicts that rating catering is more likely to occur when the replacement threat is strong, the reputational constraint formed by cross-verification is limited, and market expectations of rating inflation are self-reinforcing, which explains why potential information gains are difficult to translate into higher rating quality.
Using secondary market rating data on enterprise bonds, corporate bonds, and medium-term notes issued in China between 2014 and 2021, we find that bonds issued by firms holding multiple issuer ratings exhibit significantly larger and more upward-biased deviations of bond ratings from market-implied ratings. These results indicate that multiple issuer ratings reduce bond rating quality and manifest as rating inflation, supporting the rating catering hypothesis rather than the information gain hypothesis. To address endogeneity concerns, we conduct instrumental variable estimation based on the mandatory dual-rating requirement for super short-term commercial paper and include richer sets of fixed effects. The main results remain valid under these identification strategies and a battery of robustness checks.
Mechanism analyses support the catering channel from both internal incentives and external discipline. On the internal side, the decline in rating quality concentrates in situations where rating agencies face greater competitive pressure and weaker bargaining power, such as when industry competition is more intense, when agencies face competitive pressure from large agencies, and when issuer-agency relationships have shorter histories. Consistent with the replacement threat, agencies assigning relatively lower ratings are more likely to be dismissed by issuers subsequently. On the external side, the discipline effect of regulatory penalties and the cross-verification provided by investor-paid ratings significantly mitigate the deterioration of rating quality. Further analysis shows that the evidence does not support rating shopping as an alternative explanation, and that information asymmetry amplifies, whereas higher transparency alleviates, the adverse effect of multiple issuer ratings.
This paper contributes to the literature in three ways. First, it enriches the analysis of the economic consequences of multiple credit ratings from the perspective of rating business relationships, highlighting the strategic information production of rating agencies under competition. Second, it complements studies on the determinants of credit rating quality from the micro perspective of issuer-level rating relationships. Third, it provides a formal theoretical framework for understanding the information effects of multiple issuer ratings.
The findings offer important policy implications. Improving the effectiveness of multiple ratings cannot rely solely on increasing the number of ratings. Instead, it requires fostering a healthy competitive environment in the rating market. Regulators should strengthen supervision and market discipline, promote investor-paid ratings as verification, and improve accountability for distorted ratings. Issuers should disclose differences in rating methodologies and improve transparency, so that growth in the number of ratings translates into genuine improvements in rating quality.