The once-in-a-century global changes are accelerating evolution,and instability and uncertainty have increased significantly. These changes not only alter firms' relative use of employment intensity of capital versus labor factors, but also easily trigger firms to reduce labor inputs and lower the labor income share to achieve short-term operational sustainability, with labor income uncertainty rising accordingly. Moreover, the growing instability of labor income not only hinders China's economic transformation and upgrading but also poses challenges to achieving common prosperity. The 2025 Government Work Report underscores the importance of various channels to boost household incomes, supporting income growth and alleviate the burden on middle-and low-income groups, and improving the regular wage growth mechanism for workers. Establishing a stable labor income growth mechanism is thus not only a critical step toward achieving common prosperity but also vital for optimizing income distribution and advancing high-quality development in the real economy.
To identify the driving factors of labor income instability and prevent their adverse macroeconomic impacts, this paper constructs a systematic framework to address such issue. Specifically, we construct a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) model with heterogeneous multi-sectors to identify key factors causing labor income instability, examine their dynamic effects and term structure characteristics, thereby clarifying the dynamic feedback mechanism between labor income instability and economic fluctuations. In addition, we examine effective fiscal-monetary policy combinations to establish a long-term mechanism for sustained and stable income growth through policy simulations that consider both counter-cyclical and cross-cyclical regulatory approached.
We find that labor supply-demand mismatches, as well as skill mismatches, constitute primary drivers of labor income instability. Simultaneously, the labor income instability exerts adverse effects on aggregate demand and inflation, which exhibit both dynamic persistence and prolonged duration. In particular, the impact of labor income instability on macroeconomic fluctuations manifests a clear term structure, demonstrating relatively stronger effects in the short and medium term. Furthermore, the coordinated fiscal-monetary policies, which considers output and inflation targets, uses counter-cyclical and cross-cyclical regulation to reduce labor income instability and prevent the negative impact on macroeconomics. However, residual economic risks persist even under coordinated fiscal and monetary policy rules, necessitating the incorporation of regulatory policy measures to achieve the dual objectives of stabilizing growth and containing systemic risks.
This paper makes three marginal contributions. First, we construct a NK-DSGE model with heterogeneous multiple sectors, incorporating multiple sources of shocks and various frictions to identify potential driving factors of labor income instability. Meanwhile, we characterize the dynamic feedback mechanism between labor income instability and macroeconomic fluctuations, elucidating the generation mechanism and specific channels of economic impact. This provides evidence for understanding how labor income instability affects China's macroeconomic fluctuations and optimizing the policy regulation framework. Second, we reveal the nonlinear and structural characteristics of labor income instability across heterogeneous groups and clarify the transmission channels affecting macroeconomic fluctuations. From a group differences perspective, we explore labor income fluctuation characteristics and economic effects, particularly explaining the evolutionary mechanism and economic consequences through labor supply-demand and skill matching market frictions. This provides new insights for identifying influencing factors and establishing dynamic governance mechanisms for income instability. Third, we explore policy synergies across different policy types through counter-cyclical and cross-cyclical policy experiments targeting heterogeneous regulatory objectives. We propose a coordinated mechanism integrating counter-cyclical and cross-cyclical adjustments in aggregate and structural dimensions for fiscal and monetary policies. We provide feasible solutions to reduce labor income instability and mitigate its adverse macroeconomic impacts by establishing policy combination rules targeting aggregate demand and inflation objectives and using welfare analysis to evaluate their effectiveness and match implementation scenarios.

In the context of declining effectiveness in traditional monetary policy tools, major central banks worldwide increasingly rely on public communication to support their policy objectives. At the 15th Lujiazui Forum in 2024, Governor Pan Gongsheng emphasized that “a key feature of the modern monetary policy framework is the central bank's ability to transparently and clearly communicate its policy considerations and future outlook to markets and the public,” underscoring the critical role of central bank communication in contemporary policy frameworks. Concurrently, the government bond yield curve, serving as a benchmark rate with a complete maturity structure, constitutes an essential component of China's interest rate system. As interest rate liberalization advances and the treasury bond market matures, the term structure of government bond yields increasingly reflects forward-looking market expectations. Notably, institutions such as the European Central Bank and the Bank of Japan have integrated yield curve analysis into their monetary policy frameworks. Consequently, strengthening the modern monetary policy framework necessitates leveraging both the signaling effects of central bank communication and enhancing the transmission efficiency of the yield curve.
Existing research on central bank communication and bond yields exhibits two primary limitations: First, there is a lack of systematic examination regarding the impact of communication across the entire yield curve. Second, existing literature has a predominant focus on the unidirectional influence of communication on market yields, overlooking the critical role of market feedback in policy formulation. To address these gaps, this study integrates central bank communication into a no-arbitrage term structure model grounded in financial market principles. By adopting mixed-frequency econometric methods, we incorporate information flows combining both high-and low-frequency data. This framework enables an investigation into the bidirectional interaction between central bank communication and government bond yields in China from 2002 to 2023. Utilizing textual analysis techniques, we construct a sentiment index for central bank communication and decompose communication content into two dimensions: economic/financial fundamentals and policy guidance. This decomposition allows for quantifying their differential impacts on the interest rate term structure.
This research overcomes challenges in modeling the “signaling mechanism” of central bank communication and addressing complexities in mixed-frequency data integration. Its contributions are threefold. First, by embedding central bank communication within a mixed-frequency affine term structure model under no-arbitrage constraints, it provides a novel perspective for evaluating the efficacy of communication strategies. Second, the application of textual analysis to measure communication sentiment enables precise identification of signaling mechanisms within mixed-frequency information environments, enhancing the accuracy and timeliness of expectations management assessment. Third, through the lens of signaling effects, it elucidates the bidirectional dynamics between communication and the term structure, revealing variations in transmission mechanisms and effectiveness across different economic conditions.
Empirical findings reveal three key insights. First, a bidirectional interaction exists between central bank communication and the yield curve: positive communication sentiment lowers the entire yield curve, narrows the term spread, and elevates medium-term yields; conversely, the slope and curvature factors of the yield curve significantly inform central bank communication decisions. Second, the expectation guidance effect of communication exhibits maturity-dependent heterogeneity, exerting a stronger influence on short-to-medium maturities than on the long end. Additionally, positive communication sentiment reduces risk premiums demanded by markets. Third, with the deepening of interest rate liberalization and refinement of communication practices, the guidance efficacy of the People's Bank of China's communication has strengthened, while market feedback increasingly informs communication strategies. Currently, signaling effects predominantly derive from historical economic and policy interpretations; the guiding potential of forward-looking policy signals remains underutilized.
This study offers empirical foundations and policy insights for optimizing central bank communication strategies and leveraging market signals to enhance monetary policy effectiveness. To strengthen expectation guidance, establishing tiered and differentiated communication channels could improve market comprehension. Simultaneously, central banks could deepen the extraction of medium-to-long-term growth signals embedded in the yield curve, utilizing communication to bolster market confidence in sustainable economic health.

Exchange rate pass-through (ERPT) is a key mechanism in open economies, profoundly influencing firms' pricing behavior, international competitiveness, and macroeconomic stability. It has long drawn significant attention from both scholars and policymakers. Existing studies indicate that emerging economies generally face high levels of ERPT, with China being particularly notable with almost full pass-through. This implies that fluctuations in the RMB exchange rate are almost fully transmitted to foreign-currency export prices, leaving firms with limited pricing autonomy to buffer external shocks. In this context, while advancing RMB exchange rate marketization could enhance resource allocation efficiency, the persistently high pass-through rate intensifies concerns over the so-called “floating fear”, which means the risk of greater exchange rate flexibility may amplify macroeconomic volatility, thereby constraining deeper reform.
As global value chains (GVCs) have become the dominant mode of international trade, accounting for approximately 60% of global trade, firms increasingly operate as both importers and exporters. Their structural position within GVCs has thus emerged as a critical determinant of pricing decisions. However, much of the existing literature reduces GVC position to a single export dimension, overlooking the joint role of dual positions (import and export) along the full “import-processing-export” chain. This simplification limits our understanding of firms' actual risk-mitigation strategies and leaves key questions unanswered: Under GVC-based production networks, how does the dual identity of firms as both exporters and importers affect their pricing responses to exchange rate fluctuations？ Do export and import positions operate through distinct mechanisms？ Addressing these questions not only deepens our understanding of micro-level pricing dynamics but also offers theoretical support for overcoming the impasse in exchange rate reform and achieving high-quality foreign trade development.
To answer these questions, this paper extends the heterogeneous-firm pricing model of Berman et al. (2012) by incorporating the dual dimensions of GVC position including export upstreamness and import upstreamness, and develops a theoretical framework with testable hypotheses. The model predicts two distinct channels: First, higher export upstreamness enhances firms' market power and pricing flexibility through a market competition effect, enabling them to absorb exchange rate shocks more effectively and thereby reducing ERPT. Second, higher import upstreamness lowers cumulative trade costs through a cost-saving effect, expanding the margin within which firms can adjust prices, which also dampens ERPT. While both dimensions reduce pass-through, they operate through fundamentally different mechanisms.
Empirically, we test these predictions using a rich, firm-product-destination-year panel dataset from Chinese customs for the period 2000-2016, covering 188,000 firms, 4,038 product categories, and 172 export markets. Our findings are as follows: First, the degree of ERPT from RMB exchange rate changes to export price denominated in forein currency is as high as 91% over the sample period, implying the effectiveness of the expenditure-switching effect. Second, moving up the GVC significantly reduces ERPT, but the marginal effect of export upstreamness is stronger than that of import upstreamness. When both export and import upstreamness are at the 95th percentile, ERPT declines to 79%. Third, mechanism tests support the theoretical predictions: Export upstreamness primarily affects pricing strategies directly through the market competition channel, while import upstreamness operates indirectly by expanding price adjustment space via the cost-saving channel. Fourth, heterogeneous analyses reveal that firms with a higher share of core products or lower cumulative trade costs exhibit greater pricing flexibility and lower ERPT in response to exchange rate fluctuations.
This study makes three main contributions. First, it moves beyond the conventional unidimensional treatment of GVC position by decomposing it into dual dimensions—export and import upstreamness—and identifies two distinct transmission channels: the market competition effect and the cost-saving effect. Second, it quantifies the differential marginal effects of export and import positions, demonstrating the dominant role of export upstreamness in shaping pricing behavior. Third, it constructs a firm-level measure of cumulative trade costs, providing micro-level evidence for the amplification effect of GVC integration.
From a policy perspective, this paper offers two key implications. First, GVC upgrading should be integrated into the broader strategy of RMB exchange rate marketization. As firms move upstream in GVCs, enhanced pricing power and optimized cost structures can reduce ERPT, thereby breaking the path dependency of “stabilizing the exchange rate to stabilize exports” and creating a favorable micro-foundation for further exchange rate liberalization. Second, high-quality foreign trade development should shift from scale expansion to structural upgrading. Policies should focus on fostering core technologies, enhancing the competitiveness of core products, and reducing trade barriers for intermediate goods, encouraging firms to transition from low-value contract manufacturing to high-pricing-power, low-pass-through autonomous models, thereby supporting China's evolution from a “trading giant” to a “trading power”.
Future research could extend this analysis to regional value chains, exploring how GVC positioning within different regional production networks affects ERPT. Such work would provide more targeted policy insights for the international dimension of China's “dual-circulation” development strategy.

The 2008 global financial crisis triggered a prolonged stagnation in international trade. Weak global demand caused a slowdown in China's exports. Over the past few years, the average export growth rate was only 5%, representing a sharp decline of 20% compared to the pre-crisis period of 2001-2008. China, with the world's largest working-age population, depends critically on exports for job creation. Weak external demand and export slowdown thus pose major economic and social challenges.
This paper examines how export slowdown, driven by weak external demand, affects individuals and households. Using data from the China Family Panel Studies (CFPS) conducted by Peking University Institute of Social Studies Survey and trade data from the General Administration of Customs, we construct city-level export demand shocks to analyze the effects of export slowdown since 2012 on individuals' labor market performance, mental health, and households' economic decisions in China. We employ a shift-share instrumental variable to identify the causal effect. Specifically, to isolate the effects of export slowdown caused by weak external demand from those driven by other domestic factors, we first calculate the changes in exports of each six-digit HS product from the rest of the world (excluding China) to the rest of the world. These product-level demand shocks are then weighted by the initial export structure of each prefecture-level city, and used as an instrumental variable for city-level export shocks.
The empirical results reveal that export slowdown significantly reduces individual wages, increases unemployment among low-skilled workers, induces more individuals to engage in flexible jobs or exit the labor market, and worsens mental health. Moreover, the adverse effects of export slowdown are more pronounced among women, workers in tradable sectors, low-skilled laborers, older individuals, and those with lower occupational social recognition scores.At the household level, export slowdown leads to a significant decline in total household income. Additionally, when facing a negative income shock, households are more likely to reduce financial investments, increase borrowing, and delay debt repayment to smooth expenditures, with no significant change in saving.
Our findings offer valuable policy guidance for expanding high-standard opening-up and advancing high-quality economic development. Meanwhile, we also provide theoretical support and valuable insights for the government to address the increasingly prominent structural challenges in foreign trade. On one hand, it is essential to stabilize the foundation of foreign trade and foster new growth drivers. This includes improving export quality, optimizing export structures, and advancing trade digitization and green transformation to better align with new demands in global development. On the other hand Lastly, given that export slowdown may exacerbate household debt risks, it is advisable to establish a regular household financial monitoring system, and improve temporary social assistance programs.
This paper makes several key contributions to the literature. Firstly, in terms of research focus, this paper shifts the focus from trade liberalization to the underexplored effects of export slowdown following the post-2008 decline in global demand. Secondly, in terms of data, most existing studies on trade impacts rely on industry,region, or firm level data. In contrast, this paper utilizes micro-level individual and household data across multiple dimensions, providing a more comprehensive understanding of the direct effects of negative export shocks and their heterogeneity across different demographic groups. Thirdly, in terms of policy relevance, our findings that export slowdown significantly worsens labor market outcomes and mental health offer actionable insights for policymakers in designing measures to mitigate the adverse effects of negative export shocks. Finally, in terms of methodology, the use of a Bartik instrumental variable regression and extensive robustness checks addresses endogeneity concerns and strengthens the validity of the results.

In the era of the digital economy, data has become a core production factor, not only serving as a fundamental strategic resource to enhance national competitiveness but also emerging as a new source of comparative advantage. As the world enters a critical period of deepening technological revolutions and industrial transformation, many countries have made the big data industry a key focus of economic and social development. Through the enactment of laws governing cross-border data flows, countries are racing to secure data resources and gain a leading position in global industrial competition. Against this backdrop, how China can effectively safeguard its data sovereignty while enhancing global economic competitiveness has become a pressing and important question. At the same time, industrial policy is widely regarded as a key tool for promoting economic growth and achieving high-quality development. In the big data era, traditional industrial policy faces new challenges and opportunities. On the one hand, policymakers must consider how to harness the potential economies of scale embedded in data through well-designed policy instruments. On the other hand, they must also address the distortions in resource allocation caused by cross-border data flow barriers. Therefore, designing an effective industrial policy framework for the big data era, activating the productive potential of data while mitigating the adverse effects of cross-border restrictions, is a critical research agenda for reshaping China's comparative advantage in the global economy.
This paper develops a multi-country, multi-sector general equilibrium model incorporating external data scale economies, providing the first theoretical foundation for the design of China's industrial policy in the era of big data. In the digital economy, external scale economies of data are an important feature of production. As a result, the potential social returns to industrial policy are magnified by the presence of such scale effects. Data in production exhibits three key characteristics: first, external economies of scale, meaning that the unit cost of producing goods decreases as the volume of data increases; second, non-rivalry, with the same unit of data can be simultaneously used by multiple producers; and third, data as a byproduct of consumption, so its international mobility is determined by the data policies of individual countries.
We then calibrate our model to 38 countries and 44 sectors (including 22 tradable sectors) in 2017 using the OECD Inter-Country Input-Output (ICIO) database. Our quantitative analysis yields some key findings. First, the implementation of the EU’s General Data Protection Regulation (GDPR) reduces welfare in most countries, with EU member states experiencing the sharpest declines. The magnitude of welfare loss is positively correlated with the elasticity of data scale economies. Second, U.S. restrictions on cross-border data flows targeting China reduce welfare globally, with China bearing the greatest losses. Third, industrial policy is an effective strategy for China to counter both discriminatory and non-discriminatory cross-border data flow barriers. Data scale economies provide a key rationale for government intervention through industrial policy. The welfare gains from China's industrial policy exhibit an inverted-U relationship with the elasticity of data scale economies. Finally, in response to U.S. discriminatory data flow restrictions, retaliatory data flow policies represent another effective strategy for China to mitigate welfare losses.
This paper emphasizes several key policy recommendations. First, industrial policy serves not only as an effective tool for responding to cross-border data flow restrictions but also as a strategic instrument to safeguard national economic security and enhance the global competitiveness of key industries. Second, developing more systematic and forward-looking cross-border data policy is essential for adapting to the evolving global data governance landscape and for strengthening institutional resilience and strategic capacity in international negotiations. Third, enhancing the elasticity of data scale economies is both an intrinsic requirement for achieving high-quality economic development in China and a critical lever for securing strategic advantage in the global digital economy.
This paper makes several contributions. First, it incorporates external data scale economies and endogenous technology choices into a multi-country, multi-sector general equilibrium model, thereby enriching and extending the existing international trade frameworks. Second, it offers the first general equilibrium-based quantitative assessment of the welfare effects of both discriminatory and non-discriminatory cross-border data flow barriers. Third, it highlights the critical role of data scale economies in shaping the welfare impact of industrial policy. Finally, it proposes a concrete policy response to rising cross-border data flow barriers: by supporting key industries through targeted industrial policies, China can offset the rising marginal costs associated with constrained data scale economies and thereby enhance firms' international competitiveness.
Future research may advance along several dimensions. First, given that the cross-border data flow is largely shaped by government decisions, future work could incorporate the government's trade-offs between data security and data openness, moving beyond the assumption of welfare maximization to better analyze the interaction between data policy and industrial policy. Second, to understand the dynamic effects of such policies, future research could develop a dynamic general equilibrium model with data accumulation, offering deeper insights into the long-run implications of data policy in a multi-country, multi-sector framework.

As the basic unit of the macroeconomy, the effectiveness of risk management of real enterprises serves as the micro foundation for the prevention of macro financial risks and is directly related to the stability and health of the entire economic and financial system. Among them, ensuring that the maturity of funding sources matches that of funding needs is one of the important strategies for risk management of real enterprises. However, events such as the international financial crisis in 2008 and the COVID-19 pandemic in 2020 have made the mismatch of investment and financing maturity for enterprises a prominent issue. This mismatch will not only cause an imbalance in the term structure at the macro level, but also bring serious consequences at the micro level. Therefore, how to effectively reduce the intertemporal credit risk caused by the maturity mismatch of investment and financing has become a key issue that needs to be urgently addressed in order to improve bank supervision and prevent financial risks. Against this backdrop, the “Guidelines for the Prudent Compensation Supervision of Commercial Banks” (hereinafter referred to as the “Guidelines”) in 2010 required the deferred payment of compensation for bank executives and employees in key risk positions, which provided an important opportunity to address the above-mentioned issues.
This paper, based on the implementation of the “Guidelines” in 2010 and the data on bank-enterprise loan relationships on a transaction-by-transaction basis, constructs a multi-period difference-in-differences model at the “enterprise-bank” pairing level to investigate the impact and mechanism of intertemporal compensation contract design on the risk management of real enterprises. Research has found that the design of intertemporal compensation contracts by banks can significantly alleviate the borrowing enterprises' maturity mismatch of investment and financing and promote the risk management of real enterprises. Moreover, this effect is mainly observed in enterprises with a high risk appetite, high growth potential, and weak internal control. Mechanism identification reveals that the design of intertemporal compensation contracts by banks, through the intertemporal credit risk matching mechanism, determines the lending term structure based on the investment term structure of the demand side, thereby enhancing the degree of maturity matching of investment and financing. Economic consequence analysis indicates that by enhancing the degree of maturity matching of investment and financing, the design of intertemporal compensation contracts helps enterprises prevent both short-term and long-term risks.
Based on the research conclusions of this paper, the following policy implications can be obtained. First, regulatory authorities may consider improving the guidelines for bank compensation supervision, such as setting a compensation deferral ratio linked to the loan duration, and mandating that the cross-period matching performance of credit risks be included in the performance assessment of senior executives and the compensation deferral payment mechanism. Second, regulatory authorities should guide banks to customize credit term plans based on the characteristics of enterprises, thereby curbing the accumulation of credit risks from the source. For instance, increase the proportion of medium-and long-term loans to high-risk enterprises; encourage banks to adopt a lending strategy of “financing term covering project payback period” for high-growth enterprises. For enterprises with weak internal control, the adjustment of the loan term structure can be bound to the risk control improvement plan. Third, regulatory authorities can pay attention to the matching degree of enterprise investment and financing maturity and incorporate it into the financial risk monitoring system, so as to transform micro risk governance into the defense line of macro financial risk.
The academic contributions of this article are mainly reflected in three aspects. First, this paper focuses on the inhibitory effect of salary deferral on the maturity mismatch of investment and financing in real enterprises, reveals its micro-effect on the transmission to the real economy, and makes up for the deficiency of literature that mainly focuses on credit supply shock and unexpected effects, thereby expanding the research dimension of bank salary supervision. Second, this paper, by introducing and verifying the credit risk intertemporal matching mechanism, clarifies the logic of the intertemporal compensation contract design driving banks to match the credit and enterprise investment terms, and deepens the understanding of the role of compensation delay in affecting the risk management of real enterprises. Third, previous studies have focused on the governance role of financial regulation in current credit risks. However, the conclusion of this paper emphasizes that future regulatory reforms need to pay attention to the intertemporal mismatch of credit risks and promote the intertemporal dynamic balance of credit risks. This provides important inspiration for improving the regulatory framework and preventing financial risks.

With the extensive adoption of industrial intelligence, technologies such as industrial robots are rapidly permeating various manufacturing processes and driving enterprises' strategic upgrading. According to data from the International Federation of Robotics, China continues to lead the global market, with 290,258 new industrial robots installed in 2022, accounting for more than 50% of the global total, and maintaining a compound annual growth rate of 32.55% over the past decade. Although previous studies indicate that industrial robots significantly enhance firms' productivity and flexibility, profoundly reshaping labor markets, its influence on corporate information disclosure quality and the underlying mechanisms remains underexplored. Given that high-quality information disclosure is essential for effective capital market functioning, investigating this issue has both theoretical and practical significance.
We measure the quality of management earnings forecasts from accuracy and precision, and construct firm-level industrial intelligence adoption based on the identification of related construction-in-progress and R&D expenditures projects from financial statement disclosures. Using a panel of manufacturing listed firms in Chinese A-share markets from 2011 to 2022, our results show that higher industrial intelligence adoption significantly improves the accuracy and precision of management earnings forecasts. This positive effect is primarily driven by reduced uncertainty in production costs associated with automated production, and it is more pronounced in regions with higher statutory minimum wages, greater high-skilled labor supply, and firms with higher inventory ratios. Additionally, industrial intelligence reduces income volatility through greater production controllability, especially in firms experiencing higher product demand volatility and more diversified product portfolios. Further heterogeneity analysis indicates that the positive impact of industrial intelligence adoption is significantly stronger in labor-intensive firms or those facing intense market competition.
This study contributes to the literature in three main ways. First, it extends the literature on the economic consequences of industrial intelligence adoption by focusing on the quality of management earnings forecasts. While prior research has primarily examined how robot exposure affects firms' labor decisions and cost management behaviors, this paper investigates how industrial intelligence adoption improves cost controllability and reduces income volatility, thereby enhancing disclosure quality. Second, it enriches the set of determinants of management earnings forecasts' quality. Existing studies have emphasized external factors such as litigation risk and internal governance mechanisms like internal controls, which mainly influence intentional bias in forecasts. In contrast, this study highlights that forecast quality also depends on the quantity and reliability of information available to managers. It shows that industrial intelligence provides managers with more precise and stable operational data, thereby improving the accuracy and precision of earnings forecasts. Third, the study introduces a replicable, fine-grained metric of firm-level industrial intelligence investment derived from financial statement disclosures, offering a new template for researchers interested in tracing technological adoption.
The policy implications of this study are as follows: Corporate managers should promote the deep integration of intelligent manufacturing and accounting information systems to ensure more timely and accurate access to information, thereby improving decision-making efficiency. Firms should adopt intelligent strategies tailored to their labor structures and competitive conditions. Labor-intensive firms should proactively adopt automation to mitigate labor-market uncertainties, while highly competitive firms can leverage intelligent systems to enhance forecasting and disclosure quality. For policymakers, targeted policy support should be implemented based on regional industrial characteristics and market conditions. On one hand, improving talent policies and support services can help ensure an adequate supply of skilled labor for “human-machine matching.” On the other hand, fiscal and financial incentives should be provided to firms in highly competitive sectors. Finally, when formulating labor policies such as minimum wage standards, governments should carefully consider the “human-machine substitution effect” driven by rising labor costs, in order to strike a balance between labor protection and industrial intelligence adoption, and to achieve coordinated progress in intelligent upgrading and employment stability.

China's economy is entering a new stage driven simultaneously by population aging and rapid technological progress. On the one hand, population aging has intensified labor market imbalances and pushed up labor costs; on the other hand, breakthroughs in artificial intelligence, industrial robotics, and other hard technologies, together with deep digital transformation, are reshaping the configuration of production factors. These dual transformations provide strong incentives and momentum for enterprises to pursue workforce replacement by machines. Current academic research on the economic consequences of workforce replacement by machines remains nascent. At the macro level, existing literature mainly focuses on its impact on labor markets, finding that workforce replacement can generate both “substitution effects,” which reduce labor demand, and “scale effects,” which increase it. At the micro level, studies indicate that workforce replacement can improve production efficiency by reducing costs and stimulating innovation. However, little attention has been paid to its impact on corporate financing activities, even though financing costs are critical to the implementation of workforce replacement by machines.
Because workforce replacement by machines typically involves large-scale, long-term capital investments, equity capital costs have become a key channel to ease rigid funding requirements. Examining whether and how workforce replacement affects equity capital costs not only helps reveal investors' attitudes toward firms developing new quality productive forces through such transformation, but also tests the efficiency of capital market resource allocation. Theoretically, workforce replacement may reduce equity capital costs by enhancing production efficiency, improving firms' adaptability to market changes, and lowering operational risks; it may also improve internal controls, reduce the risk of material misstatements, and enhance earnings information quality, all of which further decrease equity capital costs. Conversely, acquiring large amounts of machinery may raise operating and financial risks, highlighting the need for rigorous empirical analysis.
Using data from Chinese A-share listed companies, this study empirically examines the effect of workforce replacement by machines on equity capital costs. The results show that workforce replacement significantly reduces firms' equity financing costs, and this finding remains robust under various tests. Mechanism analysis suggests that this effect operates through reduced business risk and improved earnings information quality. Further heterogeneity analyses reveal that the effect is more pronounced when firms or regions exhibit higher levels of digitalization, when labor protection regulations are stricter, and when a larger share of the workforce is low-skilled, indicating that workforce replacement interacts with contextual factors to reduce equity capital costs more effectively. This study provides empirical evidence on how productivity transformation affects capital markets and demonstrates that capital markets can support the development of new quality productive forces and the pursuit of high-quality economic growth.
The contributions of this paper are threefold. First, it expands research on the microeconomic consequences of workforce replacement by machines by showing that it significantly lowers equity financing costs, indicating that capital markets can recognize and reward firms' transformation toward new quality productive forces, thereby providing empirical support for the role of capital markets in facilitating high-quality economic transition. Second, it explores dual mechanisms: through enhanced production efficiency and reduced operational risk, as well as improved internal controls and earnings information quality, that explain how workforce replacement lowers equity capital costs. Third, it examines heterogeneity from the perspectives of digital technology adoption and workforce composition, offering practical implications for firms implementing workforce replacement strategies.

In the financial culture with Chinese characteristics, integrity holds the most important position. Its significance is self-evident. Excellent traditional Chinese culture emphasizes the importance of keeping promises. Because the financial industry is built on credit, it must always adhere to the spirit of contracts, which means being honest, trustworthy, and not crossing ethical boundaries. Unlike formal systems that rely on administrative regulation and economic punishment, an integrity culture internalizes social responsibility. It turns social responsibility into a form of self-regulation and moral discipline for financial firms through shared values, information flows, social control, reputation mechanisms, and common beliefs. This demonstrates a strong effect on social responsibility governance. Unfortunately, due to limitations in data availability and research methods, there is very limited research on this topic.
In recent years, the rapid development of machine learning has offered a new solution. Some scholars have started to use Large Language Models (LLMs) to measure the cultural traits of individual firms. This approach helps overcome a key problem in traditional research, where measures of culture were often too broad and cultural elements were difficult to scientifically identify. Compared to traditional static dictionaries, LLMs have two key advantages. First, they can dynamically learn from the latest data to generate answers, which reduces the problem of outdated or incomplete dictionaries. Second, LLMs are built on deep learning algorithms and neural networks, allowing them to accurately capture context and the deep relationships in language. This overcomes the potential inaccuracies of dictionary methods and improves the validity and accuracy of indicators for financial culture.
This paper uses text data from the annual reports of financial companies listed on the Shanghai and Shenzhen A-share markets. Based on an LLM, we construct an index to measure the strength of integrity culture at the firm level. We then examine if and how this integrity culture affects the CSR performance of these firms. Our research finds that an integrity culture has a significant positive effect on the CSR performance of listed financial companies. This conclusion remains valid even after a series of robustness tests, including using instrumental variables to address endogeneity and using different ways to measure variables. Furthermore, a heterogeneity analysis shows that this positive effect is stronger for certain types of firms. These include start-ups, firms in financial distress, firms with highly concentrated ownership, and those located in areas with lower levels of market development and less external attention. A study of the mechanisms reveals that integrity culture improves CSR performance primarily by reducing information asymmetry and lowering operating risks. This means that an integrity culture strengthens a company's operations and its interactions with stakeholders while weakening opportunistic and short-sighted behaviors, thereby reducing the company's business risks. This paper's policy implications are as follows: To build a strong financial nation, policymakers should promote an integrity culture to improve corporate social responsibility in financial firms. These policies must be carefully targeted, considering firm characteristics, governance, and the economic environment.
This paper makes three main contributions. First, this study is one of the first to use LLMs to construct an integrity culture index for Chinese listed financial companies from the perspective of informal institutions, filling a research gap by deeply investigating whether integrity culture influences the social responsibility performance of financial institutions. Second, after finding that an integrity-based culture impacts CSR, the paper identifies and verifies two key mechanisms: reducing information asymmetry and lowering business risk. This confirms that integrity culture works through the dual pathways of increasing information transparency and optimizing corporate governance. Finally, this research not only enriches the literature on corporate social responsibility but also provides new perspectives for interdisciplinary research that combines finance, sociology, and computer science. While existing studies have produced rich findings on CSR from the perspectives of firm-level factors, corporate governance, and the external environment, few scholars have tried to quantify financial culture or scientifically measure its effect on CSR performance. This paper addresses this gap by focusing on an integrity-based financial culture, revealing its positive impact on CSR.

Excessive speculative trading of shell companies by investors has led to inflated stock prices, distorting market pricing mechanisms and weakening the effectiveness of capital market resource allocation. The 2020 delisting reform represented a transformative shift from “soft constraints” to “hard constraints” through optimizing delisting indicators, diversifying exit channels, and simplifying procedures. Within three years of implementation, the number of forced delistings has nearly tripled compared to the previous decade. As the reform deepens, whether strengthened delisting supervision can effectively reduce the market value of shell resources, improve the quality of listed companies, and purify the capital market ecosystem has become a critical concern for scholars, practitioners, and regulators.
Using the 2020 delisting reform as a policy setting and focusing on shell resources, this paper addresses the following core questions. Can the new delisting rules improve the pricing efficiency of shell companies？ Do different delisting channels have distinct effects？ What are the underlying mechanisms？ Drawing on data from A-share listed companies from 2016 to 2023, this paper measures valuation bias by calculating the ratio of intrinsic to market value per share and estimates shell value intensity as a proxy for shell companies by computing both shell value and the predicted probability of backdoor listing. We employ a generalized difference-in-differences (DID) model to examine the impact of delisting reform on pricing efficiency. Results show that the degree of value deviation among shell companies has significantly decreased following the implementation of the new delisting rules in 2020. This conclusion continues to hold across a series of robustness tests. Further heterogeneity analysis demonstrates that the effects of the new delisting rules are more pronounced for shell companies with lower stock prices, lower net profits, those that have received qualified audit opinions, and those with a history of financial fraud violations. Mechanism analysis reveals that the implementation of the new delisting regulations has significantly enhanced corporate operational efficiency and effectively mitigated speculative behavior in the market. Regarding operational quality, the delisting reform effectively curbs manipulation of non-recurring gains, discourages financialization, boosts real investment intensity, and raises total factor productivity. On the market speculation side, we detect a marked decline in investor attention, backdoor listing speculation, and trading volume regarding shell companies, thereby squeezing the irrational premium embedded in shell prices.
This paper's contributions are threefold. First, while prior literature on China's capital market reforms has largely concentrated on “entry-side” innovations such as the IPO registration system, we shift the lens to the “exit-side” and provide a systematic evaluation of how a rule-based delisting reform reshapes shell resource pricing. By completing the entrance-to-exit analytical loop, we offer a more holistic view of institutional sequencing in emerging markets. Second, we extend the burgeoning literature on shell value formation and “regulatory premium” by demonstrating that stringent delisting supervision is an effective channel through which such premiums can be dissipated. Our evidence suggests that the so-called “control-license value” of a listing is not an immutable market feature but a policy-contingent asset whose worth declines when forced exit becomes credible. Third, we illuminate the governance function of shifting from soft to hard constraints. By squeezing arbitrage opportunities and refining risk pricing via diversified exit routes, the reform redirects capital toward high-quality firms and enhances market resilience.
Policy implications flow naturally from our results. First, strict implementation of the delisting system is essential. A standardized, timetable-driven workflow, coupled with real-time information sharing among the exchange, the CSRC, and other enforcement bodies, will sustain the credibility of hard constraints. Second, manipulation behaviors by marginal enterprises must be suppressed. A joint audit regime that pairs regulators with external auditors, backed by a stringent accountability framework and severe penalties for negligence, can mitigate last-minute cosmetic earnings management by borderline firms. Finally, investor education and risk awareness cultivation should be strengthened. Disseminating plain-language guides to delisting rules, embedding pop-up risk alerts into trading platforms, and employing big-data analytics for personalized warnings can temper speculative sentiment and foster more rational investment decisions.

Since a higher credit rating can reduce financing costs, the issuer may have an incentive to ask the rating agency to raise the credit rating. And in many cases, it is inflated above the reasonable rating level determined based on its solvency, etc., that is, credit rating inflation. Especially under the current issuer-paid model, it is difficult for credit rating agencies to maintain complete independence, and often issue inflated credit ratings in order to obtain higher rating fees in accordance with the requirements of bond issuers, which is called credit rating catering. In Chinese bond market, although from the 2006 “Administrative Measures for the Issuance of Securities by Listed Companies” to the “Interim Measures for Administration of Credit Rating Business at Securities Market” in 2019, the credit rating supervision policies have become more and more detailed, but the problem of credit rating inflation or inflated rating still plagues our country's bond rating market. The statistical results of this paper show that from 2010 to 2022, more than 80% of bond-issuing companies have obtained an AA or higher entity credit rating. Companies such as China Forestry Group, Shanghai Shimao, Yong Coal Holdings and Brilliance Auto, which have experienced bond defaults in recent years, were still issued AAA ratings before their bonds defaulted, indicating that there is a serious credit rating inflation phenomenon in practice.
According to the explanation of credit rating catering, the main purpose of rating agencies giving up independence is to obtain excess returns through rating fees. However, at present, credit rating regulators in developed countries or regions such as the United States do not require rating agencies or bond issuers to disclose commercial credit bond rating fees, and only municipal bond rating fees are concerned about rating fees (Cornaggia et al., 2023), and in China, rating fee data wasn't required to be disclosed mandatorily in practice before 2013. Due to the non-disclosure of rating fee information, and based on the difference between municipal bonds and commercial bonds, existing studies have not clearly revealed the relationship among credit bond rating fees, credit rating inflation, and credit rating quality.
On January 8, 2013, the National Association of Financial Market Institutional Investors issued the “Self-regulatory Guidelines on Credit Rating of Non-Financial Enterprise Debt Financing Instruments”, requiring credit rating agencies to annually publish last year's “Credit Rating Business Development and Compliance Operation Report” (hereinafter referred to as the “Annual Report”) before April 30 in the following year. The annual reports of each rating agency disclose the rating income information of various rating businesses, which provides a feasible research opportunity for directly examining the problem of credit rating inflation. Based on the rating income information released in the annual reports of our country's credit rating agencies from 2013 to 2022, this paper finds that high rating fees, especially the high fee deviation, lead to credit rating inflation and corresponding rating quality decline. The heterogeneity study found that among large credit rating agencies, the above tendencies converged when the degree of industry competition was low, the rating agencies were involved in bond defaults, and after the implementation of the Interim Measures for Administration of Credit Rating Business at Securities Market. The problem of low rating quality caused by the deviation of rating fees and high fees has converged. Finally, this paper also finds that there are situations where the fee is lower but the rating agency also issues a relatively high rating.
The main contributions of this paper are as follows: First, based on the institutional background of credit rating agencies disclosing Annual Report, it provides clearer and more direct empirical evidence on how rating fees affect credit rating inflation and credit rating quality, and enriches the theoretical explanation and literature of credit rating catering. Second, using the data of China's credit rating market, this paper finds that in the case of a fierce credit rating market and a limited role of reputation mechanism, competition will only aggravate the pandering behavior of rating agencies, resulting in more serious credit rating inflation, which provides a new explanation for the relationship between competition and reputation. Third, from the perspective of bond defaults and the impact of credit rating supervision policies on the reputation of rating agencies, this paper finds that government supervision will inhibit credit rating inflation, which provides new analytical dimensions and empirical evidence for revealing the relationship between government supervision and credit rating and its impact mechanism.