This paper provides a comprehensive analysis of social security participation within China's private sector,focusing specifically on the Basic Old-age Insurance for Enterprise Employees (BOIEE). As China utilizes a pay-as-you-go (PAYG) system,the participation of the active workforce serves as the linchpin for retiree welfare and systemic sustainability. However,our research reveals a significant compliance gap driven by both firm-level financial constraints and individual rational choices,challenging the feasibility of recent legal mandates.

I. The New Legal Mandate and Compliance Pressure

The landscape of labor relations in China shifted significantly on August 1,2025,with the release of the Interpretation (II) of the Supreme People's Court on Labor Dispute Cases. Article 19 effectively closed previous loopholes by declaring any “opt-out” agreements betweener employers and employees—even those made voluntarily—as legally void. This move toward strict execution seeks to rectify long-standing issues where the Labor Law (1995) and the Social Insurance Law (2011) were circumvented through informal employment. However,enforcing these standards strictly across the board may have unintended economic consequences for micro and small enterprises (MSEs),which serve as the nation's primary job creators.

II. Data Discrepancies and Systemic Bias

A major contribution of this paper is identifying the unreliability of official data. By comparing 2022 enterprise annual reports with the 2023 Enterprise Survey on Innovation and Entrepreneurship in China (ESIEC),we found that official records consistently under-report participation. Official data often reflects perfunctory reporting or intentional concealment to avoid regulatory scrutiny,whereas ESIEC data provides a more transparent view of actual operational metrics. This discrepancy highlights a systemic bias in official administrative data,making independent micro-level surveys essential for accurate policy analysis.

III. The Economic Impact of Mandatory Compliance

Using ESIEC 2025 data,we characterize a private sector where only 47.1% of full-time employees are currently covered by the BOIEE. Non-compliance is most concentrated in small-scale firms,underdeveloped regions,and the agricultural sector. The most striking finding of this research is the estimated impact on profitability. Our simulations suggest that if every full-time employee were enrolled in the BOIEE under current standards,the current average net profit margin of 8.6% would plummet to a mere 2.9%. This suggests that for a vast majority of marginal micro and small businesses,the cost of social security is not merely a line item but a threat to their existence.

IV. Individual Reluctance:A Rational Choice?

Data from the 2025 Online Survey of Micro-and-small Enterprises (OSOME) explain why employees themselves often forgo BOIEE. The study identifies two primary drivers. First,there are significant substitution effects. Many workers opt for Basic Old-age Insurance for Urban and Rural Residents or Social Insurance for Flexible Employment. Given the high mobility and occasional instability of private-sector employment,these alternatives are often viewed as more accessible or appropriate for their career trajectories.

Second,there is a low perceived investment value for the BOIEE as a financial product. Among those not enrolled,56.5% cited high premiums or personal financial hardship as the primary barrier,while another 16.7% expressed concern that they would receive negligible payouts upon retirement. Only 14.5% of respondents actually desired coverage but were blocked by employer non-provision.

V. Conclusions

The findings suggest that the compliance gap in China's private sector is a complex equilibrium maintained by both firm-level financial constraints and individual rational choices. While the 2025 judicial interpretation aims for a higher standard of social justice,policymakers must consider the fragile profitability of the private sector. Without systemic adjustments to premium rates,better insurance portability,or broader fiscal support,mandatory compliance may protect the rights of the few at the cost of the employment opportunities provided by the many. A sustainable social security system must be built on the foundation of a surviving and thriving private economy.

Fully unleashing the potential of public data as a production factor is crucial for building a unified national market and facilitating the smooth circulation of the domestic economy. To catalyze the latent value of public data,governments at  various levels  have spearheaded the establishment of public data access platforms,markedly improving the accessibility,reliability,and verifiability of government-held information. This study contends that public data access provides a unified,authoritative,and verifiable information infrastructure that materially informs supplier selection. First,the deployment of public data platforms in a destination city mitigates information asymmetry in cross-regional transactions. By lowering search costs and broadening the horizon for supplier discovery,it empowers firms to identify and onboard high-quality partners. Second,heightened transparency reduces the necessity for resource-intensive offline due diligence and continuous monitoring,thereby strengthening contractual enforceability and lowering ex-post contracting costs. Furthermore,the impact of public data access extends beyond mere structural reconfiguration. By optimizing supplier matching and improving operating efficiency,these platforms enhance a firm's broader competitive advantage. 

This study exploits the staggered establishment of city-level public data access platforms as a quasi-natural experiment,employing a staggered difference-in-differences (DID) design. Our results show that the launch of these platforms in destination cities leads to a statistically and economically significant increase in the share of procurement sourced from suppliers located in that city by listed firms. Mechanism analyses indicate that this effect operates through two primary channels:the mitigation of ex-ante information constraints in supplier selection and the reduction of ex-post monitoring and contracting frictions. With respect to the ex-ante selection mechanism,we construct three proxies for external information constraints,including the overall information environment of the supplier's city,whether the focal firm has prior procurement experience in the destination city,and whether the firm has established subsidiaries in that city. The results suggest that the positive effect of public data access on local procurement share is significantly stronger when external information constraints are higher. Regarding the ex-post supervision mechanism,we construct three variables capturing potential contracting costs,including the firm's dependence on key intermediate inputs,the credit risk borne by the firm in procurement transactions,and the trust environment of the supplier's region. The evidence shows that the procurement-enhancing effect of public data access is more pronounced among firms with greater potential contracting costs. 

Further tests on economic consequences show that as firms increase procurement from suppliers located in cities with public data access,their competitive advantage is significantly enhanced. We further examine the mechanisms through which a higher procurement share from data-access cities strengthens firms' competitiveness. First,public data access in supplier cities improves firms' ability to identify and select high-quality suppliers,thereby enhancing market competitiveness. Second,public data access enhances firms' competitive advantage by improving internal operational efficiency. This is reflected in a reduction in supply—demand mismatch risk at the supply chain level and an increase in total factor productivity at the production level. Together,improved supplier matching and higher operational efficiency explain how procurement reallocation toward data-transparent cities translates into stronger firm competitiveness. Our findings remain robust across dynamic parallel trend tests,placebo simulations,and extensive heterogeneity analyses.

This study offers three primary contributions to the literature. First,we extend research on cross-regional supply chain configuration through the lens of digital governance. While prior studies emphasize physical infrastructure,market thickness,or firm traits as determinants of procurement geography,we show how government-led improvements in information infrastructure reshape the economics of search and contracting,thereby influencing supply chain design. Second,we enrich the discourse on the economic consequences of public data access. Whereas existing research focuses on financial reporting or innovation outcomes,we shift attention to supplier geography. By articulating the dual mechanisms of ex-ante selection and ex-post supervision,we demonstrate how data governance reforms alter supply chain quality and reshape market competition. Third,from a factor allocation perspective,we advance understanding of the synergy between data and traditional production inputs. Rather than viewing data solely as a driver of innovation,we provide micro-level evidence that data-driven institutional reforms reconfigure supply chains and optimize spatial resource allocation,thereby supporting coordinated domestic circulation and sustainable growth.

With the rapid development of the digital economy,data have become a key resource driving technological transformation and industrial upgrading.Among various types of data,social credit data—encompassing dimensions such as tax records,contract fulfillment,judicial rulings,public evaluations,and transaction histories—constitute a crucial component of government data resources.When utilized effectively,such data can help reduce information asymmetry,improve the efficiency of financial resource allocation,and foster innovation-driven economic growth.However,for a long time,China's credit data have been characterized by fragmented collection,decentralized management,and inconsistent technical standards,leading to “data silos” that limit the realization of their value.The nationwide rollout of digital social credit platforms represents a significant institutional response.By standardizing data interfaces and governance rules,these platforms facilitate the real-time sharing of credit information and enable coordinated incentive and disciplinary mechanisms among government departments,financial institutions,and market entities.

Innovation is a critical strategic activity for firms.Against the backdrop of intensifying technological competition and ongoing economic restructuring,technological innovation has become a key factor in driving high-quality economic development and enhancing national core competitiveness.This paper exploits the phased construction of city-level social credit platforms from 2008 to 2022 as a quasi-natural experiment to investigate whether and through what mechanisms credit data sharing promotes firm-level innovation.Using a sample of A-share listed companies and applying a staggered difference-in-differences (DID) design,we examine changes in firms' innovation performance,measured by the number of invention patent applications.To ensure the robustness of our findings,we conduct a series of tests,including checks on identification assumptions,placebo tests,alternative measures for the dependent variable,and adjustments to clustering standards.

The results indicate that credit data sharing significantly enhances firms' innovation performance.We identify three primary mechanisms:First,enhanced credit transparency helps alleviate financing constraints,as firms with sound credit records gain better access to bank loans.Second,improved information transparency curbs managerial opportunism,prompting firms to reallocate internal slack resources to R&D activities.Third,increased trust between firms,and between firms and employees,fosters collaboration,manifested through a rise in joint patent applications and improved labor investment efficiency.These effects are particularly pronounced among firms with higher dependence on external financing,greater demand for information disclosure,and richer digital footprints.Further analysis reveals a “credit leverage” effect:the public disclosure of negative credit information strengthens disciplinary actions against non-compliant firms and incentivizes the reallocation of resources toward credible innovators.Moreover,credit data sharing enhances the efficiency of R&D investment,leading to simultaneous improvements in both the quality and productivity of innovation activities.

Compared to the existing literature,this paper makes the following contributions:First,it provides a systematic analysis of the impact of credit data sharing on firm-level technological innovation,paying particular attention to core transmission channels such as financing constraints,information transparency,and cooperation incentives.By clarifying how credit information flows influence corporate innovation behavior,it offers new insights into the micro-level channels through which the digital economy shapes real economic development,thereby extending and deepening existing research.Second,leveraging the establishment of digital social credit platforms as an exogenous shock,this paper investigates the impact of credit data sharing on corporate innovation.It finds that credit data sharing enhances firm innovation by mitigating information asymmetry in capital markets and safeguarding contractual effectiveness.This perspective,grounded in theories of capital market information asymmetry and incomplete contracts,enriches the literature related to corporate innovation.Third,by providing empirical evidence on the effectiveness and underlying logic of the government's market-based allocation of data assets,this study offers policy implications for enhancing the utilization efficiency of credit data and advancing the modernization of China's national governance system and capacity.

Promoting artificial intelligence (AI) technology application to facilitate revolutionary leaps in productivity holds strategic importance for accelerating the formation of new-type enterprise labor structures characterized by human-machine collaboration,cross-boundary integration,and co-creation sharing.However,at the micro-firm level,critical questions remain unresolved:Can AI technology application promote enterprise digital-intelligent innovation? Through which mechanisms does it enhance such innovation,and is labor skill structure optimization the key mechanism? What are the internal mechanisms driving enterprise labor skill structure optimization?

Based on this research gap,this study selects Chinese A-share listed manufacturing firms (2015—2024) as research samples and employs corporate annual reports to construct firm-level AI technology application indicators through: ①Preprocessing annual reports crawled from Sina Finance,incorporating the AI dictionary into Jieba library for word segmentation. ②Adopting the Skip-gram model to screen semantically closest words based on cosine similarity. ③Referencing Stanford HAI's 2025 AI Index Report and employing Word2vec semantic expansion to generate an AI dictionary comprising 100 terms. ④Measuring firm-level AI technology application by calculating the natural logarithm of one plus AI keyword counts.Concurrently,based on IncoPat patent database,this study measures enterprise digital-intelligent innovation output by classifying invention patents according to technical efficacy trends.

Our empirical findings reveal:①AI technology application exerts a significantly positive impact on enterprise digital-intelligent innovation,remaining robust after endogeneity treatments and robustness checks. ②AI technology application enhances digital-intelligent innovation by optimizing internal labor skill structures,improving technology affordances,and increasing knowledge diversity. ③Technology affordances and knowledge diversity constitute key mechanisms driving labor skill structure optimization,significantly incentivizing firms to increase demand for non-routine high-skilled labor.Specifically,interactive affordances demonstrates stronger positive effects than autonomous affordances; unrelated knowledge diversity exhibits stronger positive impacts than related knowledge diversity. ④Heterogeneity analysis indicates AI technology application shows more pronounced effects in non-state-owned enterprises,proves effective exclusively for large-scale enterprises,and capital-intensive industries exhibit higher sensitivity compared with labor-intensive and technology-intensive industries.

This study's marginal contributions include:①Employing MD&A textual data to capture autonomous and interactive affordances characteristics,and disaggregating knowledge diversity into unrelated and related dimensions based on patent information knowledge structures,thereby examining how different technology affordances and knowledge diversity promote labor skill structure optimization. ②Using neural network models and machine learning methods to construct firm-level AI technology application indicators based on annual reports. ③Classifying patent applications based on technical efficacy to measure “digitalization” and “intelligentization” capabilities,overcoming limitations of traditional methods focusing solely on innovation scale while neglecting quality characteristics.

Based on these findings,this study advances policy recommendations:First,guiding enterprises toward integrated collaborative AI development while strengthening intelligent upgrades of research platforms; Second,encouraging enterprises to leverage AI's role in creating new positions while reinforcing non-routine high-skilled talent creativity; Third,fully exploiting autonomous and interactive affordances to promote manufacturing transformation; Fourth,directing enterprises to apply AI technology to transcend traditional search limitations,enhancing cross-module knowledge matching efficiency.This research contributes novel empirical evidence for understanding mechanisms through which AI technology application empowers enterprise digital-intelligent innovation from the micro-firm perspective.

Against the backdrop of the convergence between the digital economy and the dual carbon goals,promoting the coordinated digital and green transformation of manufacturing enterprises—known as “dual transformation and coordination”—to enhance corporate value has become a key strategy for cultivating new productive forces and building a modern industrial system.However,enterprises still face practical challenges in advancing this dual transformation:green innovation activities often require high investment and long cycles,with insufficient internal motivation; simultaneously,digitalization and greening involve multidimensional integration across technology,management,and strategy,which can easily trigger resource conflicts and path dependencies,hindering the formation of synergistic effects.Existing research predominantly analyzes transformation drivers from single perspectives (such as policy or finance) or statically examines linear relationships between “dual transformation” and performance,failing to deeply reveal the dynamic interactions among multiple actors—such as enterprises,technology,and finance—and their underlying complex mechanisms.

To address the aforementioned research gap,this study constructs a tripartite evolutionary game model involving“core manufacturing enterprises—digital technology providers—financial institutions”.From a dynamic strategic interaction perspective,it systematically derives the intrinsic mechanisms and evolutionary pathways driving the triadic synergy of “digitalization-greening-enterprise value” through green innovation quality.The model innovatively incorporates management IT background and firm dynamic capabilities as key mediating variables,while introducing supply chain finance level as a moderating variable.Building on this framework,the study utilizes data from A-share listed manufacturing firms from 2019 to 2023.It employs a coupling coordination degree model to measure the level of “dual transformation and coordination” and applies a serial multiple mediator model to empirically test theoretical propositions.

The key findings of this study are as follows:First,the quality of corporate green innovation directly and significantly promotes the synergistic enhancement of “dual transformation and coordination” and corporate value.Second,this effect is primarily achieved through a chained mediation pathway of “enhancing management's IT background → strengthening corporate dynamic capabilities”,revealing a micro-level transmission mechanism of “technology empowerment → management adaptation → capability reconstruction”.Third,supply chain finance exhibits a double-edged sword effect:while it can directly alleviate financing constraints,excessively high levels negatively moderate the positive impact of green innovation quality on managerial IT background and the triadic synergy.This indicates that overreliance on external financing may crowd out internal capability building,triggering “resource substitution” risks.

The theoretical contributions of this study are as follows:First,by integrating dynamic simulations of evolutionary games with empirical testing,it constructs an analytical framework for understanding the complex generative mechanisms of “dual transformation and coordination”,bridging the gap between static research and dynamic processes.Second,it uncovers the chained transmission pathway of “green innovation—IT management—dynamic capabilities—synergistic value”,along with the nonlinear regulatory role of supply chain finance in this process,deepening our understanding of multi-factor coupling and interaction mechanisms.Third,it examines corporate micro-level decision-making within a “technology-management-finance” multi-dimensional ecosystem,providing a more systematic micro-foundation for understanding the synergy between digital transformation and green transformation.

Based on the research findings,this paper proposes the following tiered policy recommendations:Enterprises should focus on high-quality green innovation,optimize management teams to recruit IT-savvy professionals,strengthen dynamic capability development,and prioritize balancing external financing with internal capacity building when utilizing supply chain finance to mitigate resource dependency risks.Government departments should establish a differentiated policy incentive system,lowering transition barriers through initial subsidies and tax incentives,while refining green finance mechanisms to channel capital toward substantive innovation.Financial institutions and technology service providers should upgrade to“enabling services”,deeply embedding themselves in enterprises' digitalization and green transformation processes to achieve co-creation of value.

This study provides crucial theoretical foundations and decision-making references for enterprises to effectively advance the deep integration of digitalization and green transformation in complex environments,thereby achieving sustainable development.Future research may further explore the differentiated impacts of industry-specific characteristics and regional institutional environments on the aforementioned mechanisms,while incorporating longer-term dynamic panel data to capture the temporal patterns of “dual transformation and coordination” evolution.

In the domain of organizational research, firm growth—specifically the expansion of scale—remains a central proposition. The rapid ascent of China's economy has created a unique historical context for private entrepreneurs, reinforcing their expectations and motivations for aggressive expansion. This environment has notably exacerbated the phenomenon of entrepreneurial hubris. However, as evidenced by cases such as Evergrande and Zhongzhi, breaching the boundaries of moderate scale can precipitate severe adverse consequences. Despite the extensive body of literature on firm growth, existing studies predominantly focus on dimensions such as transaction structures, resource endowments, or cognitive constraints.

Transaction Cost Theory provides a foundational analytical framework, positing that firms internalize transactions when the marginal cost of internal production is lower than that of external market procurement. Conversely, Penrose emphasizes the determinant role of managerial capability and resource slack in driving firm growth. Subsequently, Behavioral Decision Theory incorporated the assumption of bounded rationality, revealing how cognitive limitations profoundly influence opportunity identification and problem definition. From this perspective, the spatiotemporal allocation of decision-makers' attention is viewed as a critical mechanism shaping growth strategies.

However, these theoretical perspectives largely overlook the systemic distortions exerted by entrepreneurial hubris on firm growth. Upper Echelons Theory suggests that executives possess distinct characteristics that shape divergent strategic decision-making logics. Among these traits, hubris is considered one of the most influential and is particularly prevalent among entrepreneurs. Initially utilized to explain excessive acquisition premiums, the concept of hubris has been subsequently linked to corporate risk-taking behaviors. While recent reviews call for a holistic examination of the psychological drivers of entrepreneurial behavior, the specific impact of hubris on firm scale expansion—particularly amidst complex strategic contexts like digital transformation—remains under-explored.

To address this gap, this study investigates the interactive mechanisms among entrepreneurial hubris, firm scale expansion, and firm performance, proposing three core research objectives:① To explore why hubristic entrepreneurs tend to overstate their capabilities and resource endowments while underestimating external uncertainties and constraints, thereby inflating their expectations of returns from scale expansion. ② To examine the existence of an inverted U-shaped relationship between scale expansion and performance. While early-stage growth enhances performance through bargaining power, economies of scale, and scope, exceeding a “moderate boundary” leads to managerial loss of control and resource mismatch. Consequently, this study defines “moderate firm scale” and hypothesizes that entrepreneurial hubris influences performance in an inverted U-shaped manner, mediated by scale expansion. ③ To investigate the moderating role of managerial discretion, proxied by firm age and industry munificence. Specifically, organizational inertia in older firms restricts discretion, attenuating the relationship, whereas industry munificence amplifies discretion, strengthening it.

Utilizing a sample of Chinese private listed firms from 2010 to 2019, this study empirically validates the theoretical model using panel data analysis with fixed effects (firm, year, industry, and province) and extensive robustness checks. The contributions of this study are fourfold:① revealing the positive correlation between entrepreneurial hubris and scale expansion, enriching the understanding of growth drivers; ② proposing the concept of “moderate scale” to reconcile theoretical conflicts regarding the growth-performance relationship;③ demonstrating the double-edged nature of hubris through its inverted U-shaped impact on performance; ④ clarifying the boundary effects of firm age and industry munificence. Overall, this paper constructs an integrated theoretical framework linking entrepreneurial hubris, scale expansion, and performance, offering practical implications for strategic decision-making regarding firm growth.

In recent years,brand equity has become a crucial intangible asset for enterprises seeking to enhance their core competitiveness. As an important information asset,brand equity not only profoundly influences consumer recognition and loyalty but also plays a significant role in capital markets. Beyond consumer-facing aspects,brand equity has a broader impact on a company's financial health,particularly in its relationship with stock price movements. In 2014,General Secretary Xi Jinping,in his strategic instructions on the “three transformations”,emphasized the need to promote the shift from “Chinese products to Chinese brands”,laying a strategic foundation for the development of internationally competitive Chinese brands. This vision has played a pivotal role in shifting the focus toward building stronger brand identities and enhancing the visibility of Chinese companies globally. Following this vision,in 2019,the China Central Television (CCTV) and relevant central ministries launched the “Brand Power China Initiative”,a national media project aimed at cultivating globally competitive Chinese brands through comprehensive media dissemination. This initiative underscored the national ambition to strengthen the global positioning of Chinese brands. In 2022,the National Development and Reform Commission (NDRC) and other departments jointly issued the “Guiding Opinions on Promoting Brand Building in the New Era”,which provided policy support for the high-quality and sustainable development of brand building. These efforts reflect the growing importance of brand equity not just as a business asset but also as a critical component of national economic strategy.

This paper empirically examines the relationship between brand equity and stock price synchronicity,focusing on a sample of A-share listed companies in China from 2007 to 2023. Stock price synchronicity,which refers to the degree to which individual stock prices move in alignment with market-wide movements,can serve as an indicator of information efficiency in the capital markets. Drawing on existing research,the study employs the perpetual inventory method to measure brand equity through accumulated advertising expenditure,a widely recognized approach in brand equity measurement. The regression analysis results show a significant negative relationship between brand equity and future stock price synchronicity,with companies exhibiting higher brand equity experiencing lower future stock price synchronicity. This result persists even when different depreciation rates are applied to estimate brand equity,and when brand equity is calculated using accumulated sales expenses. Additionally,to enhance the robustness of the findings,a variety of robustness checks are conducted,including the use of instrumental variables,additional control variables,higher-order fixed effects,and propensity score matching (PSM). These methods were employed to address potential endogeneity concerns and ensure the reliability of the results.

The study delves into the mechanisms through which brand equity affects stock price synchronicity. The analysis reveals that the effect of brand equity on improving stock price synchronicity is weaker in companies with more analyst coverage,higher media attention,and greater institutional investor ownership. These findings suggest that in firms with higher levels of external information dissemination and market monitoring,the potential for brand equity to reduce stock price synchronicity is less pronounced. Moreover,by introducing an information asymmetry index,the study further substantiates its conclusions. The regression results indicate that in the firms facing higher levels of information asymmetry,brand equity has a more substantial effect on reducing stock price synchronicity. This suggests that corporate governance mechanisms,driven by strong brand equity,may be one of the key channels through which brand equity impacts stock price. In particular,when information asymmetry is high,brand equity plays a vital role in improving information transparency. A high level of information asymmetry makes it difficult for external investors to obtain critical internal company information,and brand equity,as a key component of intangible assets,helps mitigate this asymmetry. By enhancing the flow of information,brand equity improves corporate governance and facilitates better market pricing,thereby lowering future stock price synchronicity. Further heterogeneity analysis indicates that the impact of brand equity on stock price synchronicity is more pronounced in firms with higher liquidity,lower industry concentration,and greater environmental uncertainty. This finding highlights the conditions under which brand equity can play an especially pivotal role in enhancing market efficiency. Economic consequence analysis shows that the accumulation of brand equity can alleviate information asymmetry and financing constraints,thereby promoting long-term investments by firms,which has a positive effect on advancing the development of the real economy.

The contributions of this study are threefold. First,it is the first to explore the relationship between brand equity and stock price synchronicity,filling the gap in the literature on the factors influencing stock price synchronicity. Second,the study highlights the role of brand equity in corporate governance and the mitigation of information asymmetry,thereby enriching the literature on the economic consequences of brand equity. Third,the findings offer valuable insights for investors,helping them more accurately assess the impact of brand equity on firm value,thereby improving the efficiency of resource allocation in capital markets. As brand equity becomes increasingly important in the modern economy,the paper underscores the need for firms to strategically manage their brand assets. Effective brand management is essential not only for building consumer loyalty but also for enhancing performance in capital markets.

Local government debt risk has emerged as a central issue in China's ongoing fiscal reform,with Local Government Financing Vehicle (LGFV) bonds serving as the primary vehicle for off-balance-sheet local government financing and attracting sustained attention from both policymakers and academics.Under the dual policy mandate of “stabilizing growth” and “preventing risks”, the manner in which local governments transmit fiscal policy signals through official documents such as government work reports critically shapes market expectations about debt sustainability.Yet existing literature has focused predominantly on the objective determinants of debt risk,with limited attention to the role of policy communication in influencing market expectations,and empirical studies grounded in the Chinese context remain particularly scarce.This paper takes a fiscal policy communication perspective to systematically examine how local government fiscal policy signals affect LGFV bond credit spreads and the underlying transmission mechanisms.

Using text analysis techniques,we construct a Fiscal Expansion Communication Index and a Fiscal Expansion Review Index by systematically identifying fiscal expansion keywords in local government work reports and classifying them into two categories:short-term debt-pressure signals and long-term growth-enhancing signals.Drawing on government work reports from prefecture-level-and-above cities spanning 1993 to 2023,combined with LGFV bond primary market issuance data from 2006 to 2023,we employ a two-way fixed-effects model for estimation.The robustness of our findings is confirmed through four sets of checks,including replacing the benchmark interest rate,substituting the dependent and independent variables respectively,and adjusting the clustering level of standard errors.Channel analysis uses newly issued LGFV bond volume and primary market bid-to-cover ratios as mediating variables to test the issuance scale channel and the investor expectations channel respectively,while heterogeneity analysis is conducted by grouping observations according to fiscal deficit ratios and fiscal transparency.

The main findings of this paper are threefold.First,the impact of fiscal policy communication signals on LGFV bond credit spreads exhibits significant heterogeneity:short-term debt-pressure signals significantly widen credit spreads,while long-term growth-enhancing signals effectively mitigate upward pressure on credit spreads.This indicates that markets engage in nuanced,differentiated interpretation of distinct types of fiscal expansion signals rather than responding uniformly to the aggregate fiscal stance.Second,channel analysis reveals that fiscal expansion policies affect credit spreads primarily through two pathways.The first is the issuance scale channel:fiscal expansion signals increase local governments' debt financing demand,leading to a larger volume of LGFV bond issuance,which markets interpret as a signal of growing debt burdens,thereby pushing up credit spreads.The second is the investor expectations channel:fiscal expansion signals dampen investor confidence in the creditworthiness and debt repayment capacity of bond issuers,reducing investment willingness,depressing primary market bid-to-cover ratios,and driving up risk premia.These findings reveal the latent debt risk effects embedded within fiscal policy transmission mechanisms.Third,heterogeneity analysis finds that the effects of fiscal expansion vary significantly with the economic characteristics of local governments:in regions with higher fiscal deficit ratios,fiscal expansion further exacerbates market concerns about debt sustainability,leading to a significant widening of credit spreads; whereas in regions with higher fiscal transparency,reduced information asymmetry enables investors to more clearly assess policy sustainability and overall fiscal conditions,allowing fiscal expansion signals to more effectively convey policy efficacy and thus alleviating upward pressure on credit spreads.Taken together,these findings suggest that the ultimate impact of proactive fiscal policy on LGFV bond credit spreads depends on whether market expectations regarding local government debt risk can be genuinely improved.

This paper makes three contributions to the literature.First,it integrates fiscal policy communication behavior into the pricing framework of LGFV bond credit spreads and reveals the inherent heterogeneity of fiscal signals,providing new theoretical and empirical foundations for domestic scholarship.Second,it enriches the understanding of the confidence effects of expansionary fiscal policy by demonstrating that market outcomes are highly sensitive to the structure of policy communication content and local fiscal credibility,reflecting the nonlinear characteristics of fiscal policy transmission mechanisms.Third,it enriches the methodological toolkit of fiscal policy research by constructing a quantifiable Fiscal Expansion Communication Index and systematically investigating the channels through which policy communication affects debt risk pricing.

The findings carry important policy implications:local governments should optimize the pace of fiscal expansion and strengthen debt risk management,with regions of already-elevated fiscal deficit ratios exercising particular caution in evaluating the necessity of new expenditure projects and avoiding excessive reliance on debt financing; fiscal transparency should be substantially enhanced through regular and comprehensive disclosure of fiscal revenues,expenditures,debt balances,and repayment plans to reduce information asymmetry; and when implementing major fiscal expansion measures,local governments should proactively communicate to the market the sustainability of their policies and the arrangements for risk management,so as to guide the formation of rational market expectations and improve the overall effectiveness of fiscal policy communication.

Against the backdrop of China's transition into a “new normal” of sustained low interest rates,its government bond market—now the world's second-largest—has witnessed unprecedented expansion (with outstanding bonds surging from RMB 23.27 trillion in 2021 to RMB 34.57 trillion by the end of 2024) alongside heightened price volatility. This volatility has exposed the failure of traditional pricing mechanisms,as conventional linear models and single-cycle analyses can no longer capture the market's evolving logic. Addressing this critical gap,this study systematically explores the structural transformation of government bond pricing drivers and their nonlinear effects across interest rate cycles,delivering both theoretical breakthroughs and practical insights.

This study clearly distinguishes between “normal-interest-rate periods” and “low-interest-rate periods” using a globally accepted criterion:a sustained overnight Shanghai Interbank Offered Rate (SHIBOR) below 2%. Drawing on daily data of key financial indicators from March 2018 to April 2025,it splits the sample into two phases:the normal-interest-rate period (March 2018–September 2023) and the low-interest-rate period (October 2023-April 2025). This division effectively captures China's first-ever sustained low-interest cycle,which is driven by cumulative monetary easing measures.

The dependent variable in the study is the CSI Long-Term Government Bond Index,a representative indicator of China's government bond market. Independent variables include SHIBOR (reflecting market liquidity and funding costs),the onshore RMB index (indicating cross-border capital flows),the gold index (representing safe-haven demand),the crude oil index (linked to inflation expectations),and the Shanghai Composite Index (reflecting market risk appetite). Methodologically,the study adopts an innovative hybrid approach:it uses linear regression (including cointegration tests and correlation analysis) to identify linear relationships between variables across different interest rate cycles,and an artificial neural network (ANN) — equipped with a hyperbolic tangent activation function and the Levenberg-Marquardt algorithm — to capture complex nonlinear relationships. This hybrid method overcomes the limitations of traditional linear analysis frameworks.

One of the study's most notable findings is the cyclical dependence of government bond pricing drivers. In the normal-interest-rate period,interest rates lose their explanatory power for bond prices (showing no stable cointegration with bond prices),while gold,the exchange rates,and the stock index emerge as the dominant driving factors. In contrast,the low-interest-rate period triggers a paradigm shift:interest rates reemerge as a critical factor with a negative impact on bond prices,and the hierarchy of driving factors becomes gold>interest rates>exchange rates. Crude oil exerts a weak negative impact in both cycles,while the stock index maintains a consistently mildnegative effect on bond prices.

Results from the ANN further reveal significant nonlinear effects that linear models fail to capture. For gold:initially,rising gold prices drive up bond prices,but this driving effect diminishes gradually; once gold prices exceed a certain threshold,gold's safe-haven attribute shifts toward a speculative one,thereby suppressing bond prices. For interest rates:short-term interest rate cuts lead to a slight decline in bond prices,attributed to “liquidity trap expectations”; however,deeper interest rate cuts trigger accelerated growth in bond prices as investors adjust their expectations. Overall,the ANN achieves far higher fitting accuracy than linear regression,demonstrating its superiority in capturing complex market dynamics.

In terms of theoretical innovation,this study is the first to formally define China's low-interest cycle and systematically compare the drivers of government bond pricing across different interest rate cycles. This fills a gap in existing literature,which often overlooks structural changes in pricing mechanisms across cycles. Additionally,the study extends multi-factor asset pricing theory by integrating nonlinear dynamics,proving that the “importance of pricing factors” is not static but varies with changes in the interest rate environment.By combining linear regression (for baseline associations) with ANN (for nonlinearity),the study overcomes the limitations of single-method approaches,providing a more holistic toolkit for analyzing complex fixed-income markets.

For investors,it identifies actionable thresholds (e.g.,gold's 2,800 level) and rate-cut dynamics to avoid nonlinear risks. For policymakers,it warns that low interest rates sharply increase bond market sensitivity to rates,narrowing conventional monetary policy space,and highlights the need to monitor gold and exchange rates as systemic risk transmitters.

In conclusion,this study reshapes our understanding of Chinese government bond pricing,proving that the market's logic undergoes a paradigm shift in low-interest environments. Its findings are not only academically significant for global fixed-income research but also practically critical for safeguarding China's financial stability amid monetary easing.