Enhancing expectation management is key to refining the modern monetary policy framework with Chinese characteristics. The Third Plenary Session of the 20th CPC Central Committee explicitly emphasized the need to “improve the expectation management mechanism”. Inflation expectations exert a profound influence on the decision-making of economic agents and the dynamics of inflation, playing a pivotal role in the central bank's efforts to guide market expectations through policy communication. Enhancing the central bank's ability to manage inflation expectations is of great significance for achieving price stability and economic growth. However, existing indexes of inflation expectations often suffer from low frequency and limited sample sizes, restricting their ability to provide granular assessments or facilitate heterogeneous analysis. These limitations hinder the identification of critical areas for improving expectation management effectiveness.
This paper employs theoretical modeling, text analysis, and empirical analysis to investigate the effectiveness of the People's Bank of China (PBC) in guiding market inflation expectations and the factors influencing this process. First, this paper constructs a theoretical model incorporating central bank inflation expectations and the formation of market inflation expectations, demonstrating the guiding role of the central bank in the formation of market inflation expectations and the factors influencing the effectiveness of the central bank's inflation expectation management. Next, based on over 60,000 Chinese macroeconomic research reports, this paper employs a combination of machine learning and text analysis to construct a weekly market inflation expectation index. Similarly, it uses central bank communication texts to construct a central bank inflation expectation index. Finally, this paper conducts empirical analysis using central bank inflation expectation indexes and market inflation expectation indexes to explore the guiding effect of central bank inflation expectations on market inflation expectations and tests whether the factors identified in the theoretical model influence the effectiveness of the People's Bank of China's inflation expectation management.
The main findings of this paper are as follows. First, the People's Bank of China's inflation expectation management can guide market inflation expectations, and this conclusion holds under various endogeneity tests and robustness tests. Second, policy space, uncertainty, and market forecasting ability all significantly influence the effectiveness of the People's Bank of China's inflation expectation management, and this conclusion is supported by both the theoretical model and empirical analysis. Specifically, the effectiveness of the central bank's inflation expectation management is better when policy space is limited, uncertainty is high, and market forecasting ability is weak. This is because, under such circumstances, the market has a higher demand for central bank information and relies more heavily on the central bank's inflation expectations.
This study provides important policy implications for the central bank to establish a sound expectation management mechanism and implement effective expectation management. First, fully leverage the coordinated role of expectation management and traditional monetary policy to enhance policy effectiveness. Second, closely align expectation management with macroeconomic and market conditions, implementing appropriate discretionary measures. Third, improve market expectation monitoring and feedback mechanisms to enhance the effectiveness of expectation management.
This paper contributes to the literature in two ways. First, by utilizing textual data and machine learning techniques, it constructs a novel index for measuring inflation expectations in China. This index, derived from macroeconomic research reports, provides a high-frequency, information-rich, and continuously updated measure, addressing the limitations of existing low-frequency and small-sample indexes. As one of the first attempts to develop a market inflation expectation index through textual big data, this study offers a crucial foundation for comprehensive and systematic assessments of the central bank's expectation management. Second, this paper extends the understanding of inflation expectation management by identifying key factors that influence its effectiveness, both theoretically and empirically. While previous studies have often been constrained by limited data availability, making it difficult to explore the drivers of effective expectation management, this research overcomes these challenges through high-frequency indexes and extensive heterogeneous analysis. The findings highlight the significant role of policy space, uncertainty, and market forecasting capabilities, offering valuable insights for enhancing the design and implementation of expectation management strategies within the broader monetary policy framework.

When a liquidity shock occurs in the interbank market, all banks tend to adopt uniform liquidity hoarding to mitigate losses caused by asset discounts due to liquidity scarcity. However, this behavior often impedes banks that were not initially affected from accessing necessary liquidity, thereby exacerbating risk accumulation and contagion. As systemically important banks, which act as stabilizers in China's banking system, it is crucial to empirically evaluate whether these banks “follow the trend” by hoarding liquidity or actively “defy the trend” by releasing liquidity to stabilize the financial market during liquidity shocks. Thus, it is of great practical significance to explore whether and through what channels systemically important banks play a stabilizing role during periods of liquidity shock.
Based on this, this paper uses micro panel data of 131 Chinese commercial banks from the third quarter of 2018 to the first quarter of 2022, adopts a continuous difference-in-differences (DID) model and difference-in-difference-in-differences (DDD) model, and starts from the liquidity hoarding to empirically test the stabilizing role of systemically important banks during liquidity shocks. It is found that when a liquidity shock occurs in the interbank market, systemically important banks do not engage in uniform liquidity hoarding. Instead, they act as a “counter-cyclical hero”, serving as stabilizers for the financial market, consistent with their role in maintaining financial stability. Specifically, (1) the phenomenon of bank liquidity hoarding triggered by liquidity shocks is primarily driven by banks with substantial interbank market risk exposure. (2) At the same time, the heterogeneous analysis of systemically important banks and non-systemically important banks found that the liquidity hoarding level of systemically important banks was significantly lower than that of non-systemically important banks. (3) Channel analysis reveals that systemically important banks mainly reduce liquidity hoarding through pledge repurchase via interbank channels and asset purchase, and the collateral required for their pledges and the financial assets purchased are mainly government bonds and policy bank bonds.
Our marginal contributions are as follows. First, this paper provides clear empirical evidence for China's systemically important banks to act as stabilizers in the financial market during liquidity shocks from the perspective of liquidity. Second, by analyzing the behavioral strategies of systemically important banks under liquidity shocks, this study reveals the specific paths and channels through which these banks stabilize financial system liquidity. This has important practical implications for understanding the functioning of China's financial market and for enhancing the central bank's strategies for market stabilization. Third, compared with previous literature, this paper introduces more exogenous and general shocks into the research design and constructs an interbank market risk exposure indicator to characterize the heterogeneous impact of liquidity shocks on liquidity hoarding, thereby enriching the literature on the relationship between liquidity shocks and liquidity hoarding.
The main conclusions of this paper have several focal suggestions. First, in view of the channel perspective of systemically important banks releasing liquidity, during periods of liquidity shock, the central bank should properly adjust the eligible collateral management framework and optimize the pricing mechanism for financial assets to reduce the costs of defying the trend for systemically important banks. Second, from the perspective of the central bank's targeted release of liquidity, when a liquidity shock occurs, the central bank should prioritize releasing liquidity to systemically important banks to reduce the cost of maintaining financial stability through the “defy the trend” channel while preventing opportunistic behavior by other banks. Third, from a broader perspective, explore and design an automatically triggered liquidity adjustment system that enforces tiered penalty rules for all banks based on the difference between their legal reserves and actual reserves, with the rule automatically executed after a crisis occurs and clearly informed to all banks.

In April 2018, the “Guiding Opinions on the Regulation of Asset Management Business of Financial Institutions” (hereinafter referred to as the “New Asset Management Regulations”)was issued, which strictly standardized the valuation rules of asset management products (AMPs) and promoted the transformation of AMPs from an expected return model to a net value-based management model that better reflects the risks of underlying assets. In practice, AMPs are the second largest participants in China's bond market, with a total holding scale accounting for a quarter of the total bond market size. Changes in valuation rules have a significant impact on the operation of the bond market. Previously, China's AMPs adopted an expected return model, using amortized costs to value assets. The product value does not change with the risk of the underlying assets but grows steadily according to the expected rate of return, making asset management institutions and AMP investors lack sensitivity to risks. After the “New Asset Management Regulations”, the proportion of net-value wealth management products has increased from less than 20% to 96.93% by the end of 2023, implying that a large number of bonds valued using amortized cost have shifted to market value measurement. How will this change the risk perception and investment behavior of asset management institutions, and how will it affect bond market liquidity, pricing efficiency, and resource allocation？ Clarifying these issues not only provides a more direct understanding of institutional investors such as AMPs but also helps deepen the theoretical understanding of the operating mechanism of the bond market.
Based on credit bond trading and issuance data from May 2017 to May 2019, the study constructs a DID model by leveraging the uniqueness of China's bond “dual market” structure and the policy shock of “New Asset Management Regulations” to analyze the impact of asset management product valuation rules on the bond market. AMPs account for a high proportion, 50%-65%, of the total bond holdings in the Exchange Bond Market, which is more affected and identified as the treated group, while the proportion in the Interbank Bond Market is only 15%-18% and identified as the control group. The research finds that: (1) There is a liquidity creation effect, where the valuation rules of AMPs have increased the bond turnover rate, leading to an additional monthly trading volume of 431.1 billion yuan in the market. However, the new transactions show a “short duration” trend, with the average financing term shortened by 4 months. (2) The spreads between bonds with different ratings have increased, manifested as a 0.22 percentage point widening of the bond pricing spread for each notch decrease in bond ratings. The mechanism analysis shows that it mainly stems from the “risk compensation motive”, where asset management institutions have increased their risk premium requirements for bonds with a lower rating. (3) The enhanced pricing efficiency in the secondary market has a signaling effect on the primary market, promoting funds flows to high-profit and low-debt enterprises, thereby improving resource allocation efficiency.
The potential innovations are mainly reflected in the following aspects: Firstly, by studying how the New Regulations on Asset Management affect the bond market, this paper enriches the research on the evaluation of financial regulatory policies, deepening the understanding of the relationship between investor behavior and bond market operation. Secondly, it expands the research on bond market efficiency from the perspective of “rating spreads”, i.e., the spread in risk premiums between low-grade and high-grade bonds, and tests two competing mechanisms by which AMPs' valuation rules widen bond rating spreads: the “safe haven effect” or the “risk compensation motive”. Thirdly, the findings suggest that the valuation rules of AMPs not only improve market liquidity and pricing efficiency but also bring about the costs of short-term preferences and increased volatility, revealing the importance of comprehensively analyzing the benefits and costs of policies.
The conclusions have the following policy implications: Firstly, attention should be paid to the vital influence of institutional investors such as AMPs on the capital market. Actively play the price discovery role of AMPs and promote the inclusion of asset management institutions in the scope of market makers. Secondly, in response to the shortening bond financing terms, improve the support mechanism for long-term bond market makers, deepen the innovation and application of debt risk mitigation tools, provide tax incentives or fee reductions for long-term bond investments, and improve performance appraisal of asset management institutions to encourage them to issue more medium-and long-term products. Thirdly, in response to the increased volatility in the bond market, establish liquidity management mechanisms, such as improving early warning of market risks and crisis response mechanisms, clarifying liquidity injection conditions and methods, etc. Set minimum liquidity requirements for asset management institutions to provide initial liquidity buffers in case of massive redemptions. Explore valuation rules that balance “accurately reflecting asset value” and “maintaining the relative stability of net asset value”.

Amid China's comprehensive green transition, a structural conflict has emerged between the urgent need for high-carbon enterprises to decarbonize and their limited access to stable external financing. As a structural financial policy tool, green finance faces persistent challenges in identifying truly green or transitioning firms, a problem rooted in green information asymmetry. This asymmetry results in a mismatch: the supply of green credit is concentrated in fully green enterprises, while demand is concentrated in high-emission firms undergoing low-carbon transition. Addressing this allocation mismatch is critical to improving both the equity and efficiency of green financial resource distribution and supporting the real economy's decarbonization pathway. Artificial intelligence (AI), as a disruptive general-purpose technology, offers new potential to address this problem. Despite AI's proven utility in areas such as credit risk modeling and robot-advisory services, existing literature provides limited evidence on how AI can enable green finance—particularly under China's policy-driven structural financial system. Prior studies often emphasize the broad effects of fintech on the real economy while overlooking how fintech enables structural financial policies to have differentiated impacts on the real economy.
To fill this gap, this paper starts from the perspective of enterprise loan cost and financing constraint and investigates whether and how AI adoption by commercial banks enhances green credit allocation by improving banks' ability to identify green or transitioning firms—what we refer to as their green recognition capability. This paper develops a theoretical model of banks' optimal loan pricing under green financial policy constraints and green information asymmetry, in which AI adoption influences banks' identification precision and thus their credit allocation behavior. The model's predictions are empirically tested using firm-level matched loan data for all A-share listed companies in China from 2013 to 2022. The results show that each 1% increase in a bank's AI adoption rate is associated with a 5.06 basis point reduction in the average loan interest rate markup, with a more pronounced reduction of 7.28 basis points for green firms, while the effect is statistically insignificant for non-green firms. From the perspective of financing constraints, each 1% increase in AI adoption is associated with a 1.61 basis point decrease in firms' financing constraints measured by the KZ index, driven mainly by a 1.40 basis point reduction for green firms. These findings suggest that AI adoption enables banks to better serve green enterprises without adversely affecting non-green firms, effectively reducing both loan costs and financing frictions for green firms. Crucially, this paper finds that the impact is significantly greater for firms with lower green information transparency, providing empirical support for the mechanism whereby AI strengthens banks' green recognition capability and enhances real economy outcomes. The effect is more pronounced for banks with faster loan growth and in regions with higher environmental spending.
Based on these findings, three policy recommendations are proposed. First, promote the deep integration of AI and green finance by strengthening banks' green identification capabilities through unified green standards, transparent data infrastructure, and supportive regulatory frameworks. Second, accelerate the development of transition finance standards by leveraging AI to identify and support decarbonization in high-carbon sectors, thereby reducing banks' perceived risk premiums and improving credit access for transitioning firms. Third, adopt differentiated, region-and sector-specific policies to pilot AI-powered green finance reforms, enabling scalable policy experimentation and tailored support for diverse transition pathways.
This paper contributes to the literature in three ways. First, it provides theoretical and empirical evidence on the feasibility and necessity of integrating AI with green finance, a topic at the frontier of interdisciplinary research between economics and artificial intelligence. Second, it expands the analysis of structural financial policy heterogeneity, demonstrating how targeted financial instruments, when empowered by technology, can yield differentiated real economy outcomes. From the perspective of artificial intelligence enabling green finance, it provides direct evidence for China's comprehensive green transformation. Third, it offers a novel perspective on addressing the financing gap for non-green firms during green transition, and provides actionable insights into how AI can help overcome green information asymmetries and improve the precision of financial support for decarbonization in high-emission sectors.

Rural credit institutions often face conflicts between financial and social targets when providing inclusive financial services through traditional ways. Digital transformation offers new possibilities to alleviate this dual-target conflict. However, constrained by insufficient capital, talent, and technology, rural credit institutions find it difficult to achieve a digital breakthrough independently. Provincial credit cooperatives unions(PCCUs) have gradually become the key drivers of digital transformation for rural credit institutions. Data show that by the end of 2023, 26 provincial-level unions had explicitly stated their intention to leverage their scale advantages to build unified digital technology platforms to support the digital transformation of affiliated rural credit institutions. The fundamental aim of rural credit institutions in actively promoting digital transformation is to enhance operational capabilities and improve the quality and efficiency of services for agriculture, rural areas, and farmers (“Sannong”) through digital technologies. Therefore, whether the current digital transformation driven by PCU-based “big platform” is effective, whether it can genuinely enhance their capacity to serve "Sannong", and it can help rural credit institutions balancing financial and social targets are pressing practical questions that require answers.
From the perspective of balancing financial and social targets, this paper utilizes micro-level data of rural credit institutions from 2011 to 2022 to examine the effectiveness of digital transformation under the PCCU “big platform” mode. The study finds that, first, the digital transformation under the PCCU “big platform” mode not only enables rural credit institutions to balance financial and social targets but also facilitates mutual reinforcement between the two objectives. Second, the mechanism through which this mode promotes such dual-target balancing lies in its ability to enhance the operational efficiency and agricultural support efficiency of rural credit institutions, reduce operational and management costs, and strengthen risk control capabilities. Third, although the digital transformation under the “big platform” mode can raise the interest spread on the loan side and thereby expand profit margins, it does not have a significant effect on the deposit-side interest spread, which poses a challenge to the realization of dual-target balancing for rural credit institutions. Fourth, the digital transformation driven by the PCCU “big platform” mode is more effective for small-scale institutions, those located in regions with higher levels of digital financial development, and those in eastern China.
The contributions of this paper are as follows: First, while existing studies mainly focus on the digital transformation of large or listed banks, this paper turns its attention to the digital transformation modes and outcomes of relatively weaker rural credit institutions, thereby enriching the literature on the digital transformation of commercial banks. Second, although a few studies pay attention to the digital transformation of rural financial institutions, they tend to focus on individual institutions, overlooking the coordinating and supporting role of PCCUs in the transformation process. This paper focuses on the PCCU digital “big platform” and investigates the financial and social effects of PCCU-driven digital transformation of rural credit institutions, which helps broaden existing research. Third, differing from the existing literature that often treats financial and social targets in isolation, this paper employs a system of simultaneous equations to evaluate the effects of digital transformation from the perspective of balancing both targets and considers their mutual influences, thereby enhancing the robustness of the analytical conclusions.
The policy implications of this paper are as follows: First, it is essential to fully leverage the coordinating function of PCCUs to advance the digital transformation of rural credit institutions. In promoting reforms to the PCCU system, efforts should be made to strengthen its service and coordination capacity and harness its advantages in resource allocation, technological integration, and platform development through scale and intensification. At the same time, the orderly promotion of the establishment of provincial-level joint-stock rural commercial banks or provincial rural commercial banks should be pursued to further enhance their capabilities in digital system development and technological integration. Second, differentiated digital development paths should be explored to improve the profitability of rural credit institutions. While promoting unified digital transformation under PCCU coordination, over-reliance on PCCU digital platforms should be avoided. For rural credit institutions with a specific scale and capability, independent efforts to develop and apply digital technologies through multiple ways should be encouraged to achieve differentiated digital development. Third, a favorable external environment for the digital transformation of rural credit institutions should be cultivated. When formulating digital development policies, regulatory authorities should adopt targeted measures, increase investment in digital infrastructure in regions with low levels of digital development and weak financial foundations, enhance the provision of necessary financial, human, and technical support, and promote balanced digital development across regions.

Climate change is deeply affecting the global real economy. As a responsible major country, China attaches great importance to climate issues, not only elevating the achievement of the “dual carbon” goals and actively responding to climate change to the national strategic level, but also directly implementing a series of climate policies and achieving positive results. However, it is important to note that firms often cannot accurately predict whether, when, or to what extent government agencies will adjust climate policies. Therefore, climate policy carries a high degree of uncertainty. From an economic perspective, the macro-level climate policy uncertainty index has been rising sharply in recent years, suggesting that existing research on this issue remains limited.
This paper examines the impact of climate policy uncertainty on the real economy by focusing on corporate financial asset allocation. The results show that a one standard deviation increase in climate policy uncertainty leads to a 6.66% standard deviation increase in the level of financial asset allocation. Mechanism analysis reveals that climate policy uncertainty increases the risks of both traditional physical assets and green physical assets, which drives firms to adjust their asset structures. Heterogeneity analysis further indicates that the effects of climate policy uncertainty are compounded by physical climate risks, especially in firms with weaker risk governance and limited external guidance. The economic consequences test demonstrates that the shift toward financial asset allocation caused by climate policy uncertainty is not conducive to firms' green transformation and high-quality development.
The marginal contribution of this paper is mainly reflected in the following three aspects: First, this study finds that, similar to physical climate risks, climate policy uncertainty can also reduce the return on real investments, leading firms to increase their financial asset allocation. The finding adds useful evidence to the growing literature on how climate risks affect the real economy. Second, as China moves into a key stage of green and low-carbon transition, the influence of climate policy on the economy has become more important. This paper adds to the literature on policy uncertainty and corporate investment by focusing on the specific uncertainty related to climate policy, rather than using general economic policy uncertainty indicators. Finally, this paper clarifies the inherent mechanism of climate policy uncertainty leading to corporate financial asset allocation, that is, climate policies with high uncertainty are not only not conducive to the green transformation of corporate physical assets, but also increase the risk of traditional physical assets.
This paper provides the following policy implications. First, for climate policymakers, it is important to consider not only the strictness or flexibility of individual climate policies but also the consistency and coordination among newly introduced and existing policies. Improving policy coherence can help reduce uncertainty and ease the investment challenges faced by firms in the real economy. Second, it is essential to recognize the diverse and systemic nature of climate risks, as well as the interconnections among different types of specific climate risks. Policymakers should make full use of the guiding role of long-term institutional investors and government-led environmental policies to build a multi-level governance framework that involves government, markets, and enterprises. Finally, for firms, it is necessary to adopt a long-term perspective, continuously strengthen their resilience to climate risks, and build a solid foundation for a low-carbon transition. This can help reduce short-term speculative behavior, such as excessive reliance on financial asset allocation, and support more sustainable business strategies.

The report of the 20th National Congress of the Communist Party of China emphasizes that “high-quality development is the top task of building China into a modern socialist country in all respects.” As a vital force in advancing Chinese modernization, the private economy serves as a crucial foundation for achieving high-quality development in China. However, financing difficulties for small and medium-sized enterprises (SMEs) remain a persistent challenge to China's economic growth. Currently, debt financing, predominantly through bank loans, constitutes the primary funding channel for Chinese enterprises. Yet, SMEs account for less than 30% of the total outstanding bank loans, and their financing costs are significantly higher than the average level. Therefore, alleviating the financing constraints of SMEs is a critical measure for promoting high-quality economic development, carrying profound strategic significance and practical value.
Extensive research has been conducted on the factors influencing SMEs financing. However, these studies have largely overlooked the impact of listed firms' “shell” resources on SMEs bank loans based on the capital market in China. In China's capital market, listed firms' shell resources hold substantial value, leading to a preferential allocation of limited local credit resources to listed companies. This, in turn, creates a crowding-out effect on bank loans for non-listed firms, ultimately exacerbating SMEs' financing difficulties and resulting in high costs. Building on prior research, this study further examines whether the value of listed firms' shell resources affects the allocation of bank credit, thereby contributing incrementally to the existing literature.
On November 30, 2013, the China Securities Regulatory Commission (CSRC) issued the “Notice on Strictly Implementing the Standards for Initial Public Offerings in Backdoor Listing Reviews”, prohibiting backdoor listings on the ChiNext board, which consequently eliminated the shell value of ChiNext-listed firms. Leveraging this exogenous policy shock, this paper employs a difference-in-differences (DID) approach, using firms listed on the National Equities Exchange and Quotations (NEEQ) from 2009 to 2018 as the treatment group and ChiNext-listed firms as the control group, to examine the impact of listed firms' shell resources on SMEs bank loan costs. The empirical findings reveal that: (1) After the loss of shell value among ChiNext-listed firms, the bank loan costs for local NEEQ firms decrease significantly. (2) Mechanism analysis indicates that the depletion of shell resources among ChiNext-listed firms significantly improves loan accessibility for local NEEQ firms and alleviates their financing constraints. (3) Heterogeneity analysis shows that these effects are more pronounced in regions where ChiNext-listed firms have greater access to credit resources and among firms with higher risk and weaker corporate governance.
The contributions of this study are threefold: First, it innovatively examines the determinants of SMEs financing difficulties and high costs from the perspective of listed firms' shell resources. Second, it extends the research on the economic consequences of shell resources by focusing on bank loan costs. Third, it deepens the understanding of the factors influencing corporate financing constraints through the lens of shell resources. Given the adverse effects of shell resources on SMEs financing, this study proposes the following policy recommendations to alleviate financing difficulties and promote high-quality economic development: (1) Further advance the IPO registration-based reform and improve the delisting mechanism to reduce the value of shell resources, thereby mitigating their crowding-out effect on SMEs bank loans. (2) During the transitional period before the full implementation of registration-based reforms and a mature delisting system, strengthen targeted support policies for SMEss to counteract the negative impact of shell resources. (3) Develop patent technology markets and high-standard technology trading platforms to integrate technological and financial innovation, alleviating financing challenges for high-tech SMEs. (4) Enhance the construction of SMEs credit databases to dynamically monitor policy effects and provide data support for future reforms and research.
Future research could extend this study in several directions: First, as the ban on backdoor listings on the ChiNext board was lifted in 2019, and the board transitioned from an approval-based to a registration-based IPO system, subsequent studies could track the long-term evolution of shell resource value and its impact on SMEs financing, as well as the role of market self-regulation under full registration-based reforms. Second, while this study proposes policy measures such as deepening capital market reforms and enhancing SMEs support, the actual effectiveness and feasibility of these policies require further empirical validation. Future research could evaluate the implementation effects of relevant policies to provide evidence-based insights for policy optimization.

In recent years, the global supply chain has been undergoing a phase of accelerated restructuring, introducing considerable uncertainty to the participation of Chinese firms in the international division of labor and the realization of export growth. To this end, deepening domestic openness and refining the division of labor within the country to restructure industrial and supply chains, thereby enhancing export competitiveness and achieving steady yet progressive export growth, is not only a fundamental requirement for building a unified national market but also a crucial pillar for advancing the new development paradigm of mutually reinforcing “dual circulation” featureing positive interplay between domestic and international economic flows. Both macro-level data and micro-level studies (e.g., Kee and Tang, 2016) indicate that achieving steady and sustained export growth increasingly depends on the domestic configuration of intermediate goods production. In this context, the paper adopts a novel focus on firms' cross-regional sourcing of intermediate goods to examine the effects and mechanisms of market integration on export growth, providing theoretical foundations and policy guidance for reducing costs and increasing the efficiency of the domestic industrial chain, and promoting sustainable foreign trade development.
From a theoretical perspective, this paper revises the framework proposed by Halpern et al. (2015) to construct a unified model linking domestic division of labor with export growth. Within the context of building a unified national market, it examines the impact and transmission mechanisms of increased cross-regional sourcing of intermediate goods by firms on export performance. Theoretical analysis suggests that a higher share of cross-regional sourcing, as facilitated by market integration, promotes export volume through productivity gains, while also influencing export diversification through the combined effects of productivity improvement and reduced incentives for R&D investment.
From an empirical perspective, this paper adopts the lens of firms' cross-regional sourcing of intermediate goods to investigate the impact and transmission mechanisms of unified market development on export growth, using matched data from China's Customs Statistics and listed company databases. The results show that a one-percentage-point increase in the share of cross-regional sourcing of intermediate goods leads to a 0.2845% increase in the quantity of firm exports to a specific destination-product pair, and a 0.0737% increase in the number of product varieties exported to a given destination. The former effect is primarily driven by productivity gains, while the latter reflects the combined influence of productivity improvement and reduced incentives for R&D investment.
This study carries significant policy implications. First, it highlights the need to strengthen domestic openness by dismantling inter-regional market segmentation and advancing the development of a unified national market. Second, it underscores the importance of improving both the quality and diversity of domestically produced intermediate goods to support the modernization of domestic industrial chains and safeguard the security and resilience of national supply chains. Third, it is essential to enhance cross-regional sourcing of intermediate goods in line with firm-level heterogeneity, in order to boost productivity and promote export growth.
The marginal contributions of this paper are as follows. First, it addresses the limitations of existing literature that primarily measure market integration from a macro perspective by identifying the increase in firms' cross-regional sourcing of intermediate goods as a salient indicator of the development of a unified national market. This allows for the establishment of a firm-level analytical lens to examine the relationship between domestic market integration and international export growth. Second, this study extends the theoretical framework of Halpern et al. (2015) by incorporating domestic specialization in intermediate goods and international exports, and further introduces firms' decisions regarding product line expansion. In doing so, it develops an innovative theoretical model linking the construction of a unified domestic market with export growth through the lens of cross-regional intermediate input sourcing, thereby enriching the theoretical foundation for the dual circulation pattern. Third, adopting this micro-level perspective, the paper empirically investigates the impact of unified market development on both the intensive and extensive margins of firm-level exports, alongside its transmission mechanisms and heterogeneous effects. The empirical findings not only lend strong support to the theoretical propositions but also help mitigate potential upward biases in previous studies that rely solely on macro-level measures of market integration when assessing firm-level export performance.

As an essential transportation infrastructure designed to enhance urban efficiency, subways worldwide have long struggled with persistent operating losses. This is also the case in China, where subway construction and operation rely heavily on government subsidies and debt financing. However, despite growing fiscal pressure on local governments in recent years, many cities prioritize subway expansion in their urban development plans, resulting in ongoing debates about the costs and benefits of subway projects. Against this backdrop, it is essential to analyze the economic benefits of subway construction in Chinese cities, which can help understand the government's policy goals better and provide practical implications for local governments to improve their infrastructure plans. Moreover, existing studies have not fully explored the economic externalities of subway systems, especially from the perspective of the cost-reduction effect. This study addresses this gap by examining how subway opening influences firms' labor costs.
We argue that improved commuting efficiency from subway opening can reduce labor costs for nearby firms in two ways: by enhancing firms' wage bargaining power and by lowering employees' expectations for monetary compensation. For firms, improved commuting conditions and greater accessibility around subway stations help attract a larger pool of workers and expand the potential labor supply. Drawing on the labor market supply-demand framework and wage-bargaining theory, a larger applicant pool intensifies job seekers' competition, enhancing firms' bargaining position in wage negotiations and ultimately reducing labor costs. For employees, improved commuting efficiency provides non-monetary benefits such as higher job satisfaction and improved mental well-being, which may lower employees' expectations for monetary compensation. In addition, the subway's capacity to support efficient and long-distance commuting enables employees to reside in areas with lower living costs, thus increasing their willingness to accept lower wages.
This study uses manually collected data on the opening dates and geographic coordinates of subway stations across Chinese cities to construct a dataset tracking subway opening within a 500-meter radius of the office locations of A-share listed firms from 2003 to 2019. The baseline results show that opening a nearby subway station leads to an average 5.63% reduction in firms' labor costs, equivalent to an annual decrease of 6,163.10 yuan in compensation per employee, highlighting the significant economic benefits of subway expansion. Mechanism analysis suggests two primary channels. First, subway access enables firms to tap into a larger labor pool and attract more job applicants, intensifying competition among workers and thereby strengthening firms' bargaining power. Second, compared to other commuting options, subways offer employees non-monetary well-being, such as mental health and job satisfaction, and also help lower their basic living expenses,further reducing their reservation wages. In addition, the cost-reduction effect is more pronounced for subways with high speeds and multiple transfer routes. However, the effect is relatively limited in areas with favorable commuting conditions, low living costs, or low population density. Finally, the study finds that the labor cost savings from subway access can facilitate further investment by firms.
The marginal contributions of this study are as follows. First, this study provides new evidence on the economic externalities of subway systems from the perspective of firms' labor costs. This not only complements the existing literature on the economic outcomes of subway construction but also deepens public and policy-level understanding of its broader economic value. Second, the study explores the link between subway opening and firms' labor costs by focusing on commuting efficiency, thereby extending the academic literature on the determinants of labor costs. Third, the study finds that subway opening helps reduce firms' operating costs, offering valuable policy implications for leveraging infrastructure investment to promote high-quality enterprise growth, especially amid growing concerns over China's economic deceleration.
This study proposes several policy implications. First, stakeholders should adopt a more balanced view of the economic value of subway systems. For example, the media are encouraged to take a more evidence-based approach and highlight the social and economic benefits of subways to improve public understanding of transport infrastructure. Second, firms should make full use of the opportunities created by improved public transportation. They can seek government support to improve local transport and prioritize office locations with better commuting conditions. Third, local governments should approach subway planning more carefully by weighing fiscal costs against potential economic benefits. Effective planning should consider factors such as passenger demand and existing transport infrastructure to avoid inefficient investments and fiscal risks.

Accelerating the creation of a new development dynamic requires improving the long-term mechanism for expanding consumption, which can better leverage the wealth effect of capital markets on household consumption. Given that housing assets dominate the portfolio of Chinese households, domestic discussions about the wealth effect have primarily focused on the housing market, while research findings regarding the wealth effect of China's stock market remain inconsistent. In recent years, the influence of the stock market on household welfare has been steadily growing. According to data from the National Bureau of Statistics and the China Securities Depository and Clearing Corporation Statistical Yearbook, Chinese households' participation rate in the stock market rose from less than 5% before 2014 to 14.99% in 2022. This paper aims to address three core research questions: Does China's stock market exhibit a wealth effect？ If such an effect exists, what are the underlying transmission mechanisms？ Do stock market fluctuations differentially affect consumption behavior across heterogeneous household groups？
This study employs data from the China Household Finance Survey (CHFS) and applies the Propensity Score Matching-Difference in Differences (PSM-DID) method to empirically examine the wealth effect in China's stock market. The results demonstrate a statistically significant wealth effect in China's stock market, with the baseline findings remaining robust after conducting various tests, including alternative dependent variables, consideration of indirect stock market participation, and external validity analysis. The transmission mechanisms primarily operate through enhancing household property income and reducing the motivation for precautionary savings. Subsequently, heterogeneous analysis from the perspective of consumption inequality reveals that the wealth effect is more pronounced among households with lower consumption levels, lower income, limited financial literacy, and lower liquid asset holdings. Finally, through scenario simulations, this study estimates that when China's stock market participation rate reaches 70% with returns either matching the average performance of major economies' stock indices or growing in tandem with GDP, the annual boost to household consumption expenditure would range approximately between 500-700 billion yuan.
This study makes three principal contributions to the literature: First, it not only empirically confirms the existence of a wealth effect in China's stock market but also extends the research on how property income influences household consumption. Second, the study examines the heterogeneity of the stock market wealth effect across micro-level households, revealing the differential impact of transitory income shocks on consumption behavior. These findings provide further empirical validation and extension of the liquidity constraints theory (Zeldes, 1989) and the buffer-stock saving model (Carroll, 1997). Third, by constructing a counterfactual analysis framework, the study simulates the stimulative effect of stock market development on household consumption under various scenarios, offering evidence-based policy insights for fostering healthy and stable capital market development.
This study proposes three key policy implications. First, regulators must prioritize maintaining the healthy and stable development of China's stock market, as it constitutes the fundamental prerequisite for the wealth effect. The current underdeveloped regulatory framework and financing-oriented growth model have long constrained market vitality, while persistent weak performance further discourages new capital inflows, creating a vicious cycle. To address this, regulators should strengthen the equilibrium between investment and financing functions, impose stricter penalties for financial misconduct, including fraud and insider trading, accelerate delisting mechanisms to improve the quality of listed companies, and institutionalize safeguards for sustainable development. Second, with a well-functioning stock market, households should be encouraged toward indirect participation through mutual funds, given our empirical findings that fund-based investment boosts consumption willingness more effectively than direct stock ownership. This approach accommodates China's current low financial literacy by offering professional management and lower barriers to entry, though it necessitates parallel improvements in fund supervision and investor education. Third, since our analysis identifies disposable income and precautionary savings as core consumption determinants, stimulus policies should focus on raising incomes and strengthening social security, with particular emphasis on low-income, financially underserved, and liquidity-constrained households that demonstrate higher consumption sensitivity to income shocks. Reforms in income distribution and social security should specifically target these groups to maximize consumption potential.

Existing research has primarily focused on the behavioral patterns of stock investors, while studies on mutual fund investors have largely centered on fund managers. Within the limited literature on individual mutual fund investors, most attention has been directed toward the redemption behavior, with relatively little emphasis on buying behavior. A key challenge in this area is the difficulty of defining the set of funds available for potential purchase. This issue can be mitigated by focusing on repurchase behavior, as previously redeemed funds form a naturally observable and well-defined choice set. Although prior studies have explored stock investors' repurchase behavior, notable differences between stock and fund investors—in trading frequency, investment strategies, performance, and disposition effect—necessitate further investigation into whether mutual fund investors exhibit similar behavioral biases.
We utilize micro-level data from a major anonymous online mutual fund distribution platform. Our analysis is based on complete transaction and portfolio records from 200,000 individual investors between 2018 and 2019. Using a Logit model, we empirically examine investors' repurchase decisions and assess the influence of the recency effect. Grounded in behavioral finance theory, this paper systematically explores the cognitive mechanisms underlying mutual fund repurchase behavior. Our findings reveal that investors prefer to repurchase funds previously redeemed at a gain compared to other previously redeemed funds. Importantly, the recency effect substantially influences individual investors' repurchase decisions. Specifically, recent transactions involving other funds—whether subscriptions or redemptions—significantly reduce investors' propensity to repurchase previously redeemed funds. Additionally, a higher frequency of recent transactions further decreases the likelihood of repurchasing previously redeemed funds. Finally, the repurchase decisions of experienced investors are less affected by the recency bias.
This paper makes several contributions to existing literature. First, our study shifts the focus from the widely studied selling behavior of individual investors to an in-depth analysis of their buying behavior in the mutual fund context, extending the understanding of investor trading activities beyond single-security decisions. Second, we introduce the recency effect—a relatively underexplored cognitive bias—into the study of mutual fund trading behavior. Based on the research of Nofsinger and Varma (2013), this paper further refines the measurement of recency effect from two dimensions of transaction type (subscription and redemption) and transaction frequency, and comprehensively examines its influence on repurchase behavior. Third, unlike prior studies that focus on single-security decisions, we adopt a portfolio-level perspective to examine how transactions in other funds influence repurchase decisions, offering new insights into cross-security behavioral patterns.
Based on our findings, we offer three policy recommendations. First, investor education and emotional management programs should be strengthened to reduce irrational behaviors driven by cognitive biases such as the recency effect. Second, fund distribution platforms should enhance their customization and intelligent decision support functions. Fund sales institutions should make use of algorithm recommendation and intelligent reminder functions, based on investors' historical transaction data and investment preferences, to prompt them to pay attention to long-term performance and risk, avoid excessive attention to recent transactions and short-term performance, reduce the negative impact of recency bias, and promote the reasonable allocation of investment portfolio. Third, regulatory authorities should improve investor transaction data infrastructure and promote controlled data sharing. This initiative would support behavioral finance research and evidence-based policymaking, ultimately contributing to the sustainable development of the mutual fund market.
Future research can extend this study in several meaningful directions. First, comparing the repurchase behavior of online and offline mutual fund investors could help evaluate the generalizability of the findings. Second, investigating differences in repurchase behavior between passive and active fund investors may clarify how fund type shapes investor decision-making patterns. Third, examining the relationship between repurchase behavior and investment performance can offer insights into the long-term impact of cognitive biases on investor outcomes. These extensions would enhance the theoretical understanding of mutual fund subscription behavior and provide empirical evidence to inform fintech development and policy optimization.