In the year 1023 AD, the Song government established the Jiaozi authority which is responsible for Jiaozi issuance and exchange in Chengdu, Sichuan, marking a full millennium this year. Jiaozi represents a significant financial innovation in China's history, initiating a grand experiment in the history of currency with banknote serving as a credit medium. The successes and failures of Jiaozi are highly instructive for modern monetary policy and currency stability. Unconstrained issuance of paper money inevitably leads to inflation and devaluation. A sound, sustainable monetary system conducive to economic stability and growth emerges from competition. Today, most countries adopt a system where paper money serves as a national credit currency (fiat money), necessitating a clear monetary policy aims to maintain currency value stability, thus highlighting the importance of establishing and refining a modern central banking system. In studying the historical contributions of Jiaozi, it is crucial to pay attention to details, particularly the commercial credit behind private-issued Jiaozi, reflect on the reasons for the ultimate failure of government-issued Jiaozi due to fiscal over-issuance, and focus on researching the series of credit support system arrangements behind Jiaozi, such as reserve requirements, issuance quote limits, segmented issuance systems, circulation payment systems, and other details like redemption discount rates and the scope of circulation.

With the sustained growth of China's economy and its increasing openness, the renminbi (RMB) has been rising in importance in the international monetary system. The RMB is increasingly becoming the “anchor currency” for the exchange rates of other countries. However, there is no systematic analysis of the RMB as an anchor currency, especially from the perspective of the international monetary system, comparing the differences and influencing factors of the RMB versus other international currencies as an anchor. In a recent paper, Ilzetzki et al. (2019) evaluate the anchoring role of the US dollar, but they do not propose a theory of endogenous anchor currency choices nor quantitatively assess the anchoring effect of the RMB. Therefore, given this gap in the literature, it is of considerable importance to study the anchoring effect of the RMB from both theoretical and empirical perspectives.
The report of the 20th CPC National Congress emphasizes accelerating the establishment of a new development pattern, with domestic circulation as the main force and mutual promotion of domestic and international dual circulation. Under this new development pattern, RMB internationalization plays a crucial role in connecting domestic and international dual circulation. The Central Financial Work Conference of 2023 proposes the goal of building up China’s financial strength, emphasizing the need to expand high-level financial openness and steadily promote the internationalization of the RMB. In this context, studying the impact of factors such as the size of the economy, trade costs, and financial openness policies on the anchoring effect of the RMB can provide policy insights for the subsequent steady and prudent promotion of RMB internationalization in China.
Based on both theoretical modeling and empirical analysis, this paper systematically investigates the endogenous selection process and factors that influence how a country's currency becomes the anchor currency for the exchange rates of other countries. The theoretical framework builds on optimal currency area theory and innovatively constructs a three-country model for the endogenous selection of the optimal anchor currency, analyzing how a country weighs the choices between two anchor currencies. By comparing the characteristics of anchor currency issuing countries, we explore the mechanisms behind the selection of anchor currencies by non-anchor currency issuing countries, the decision-making process for the optimal anchoring degree, and key influencing factors. We find that the larger the economic size and the lower the bilateral trade costs of the anchor currency issuing country, the greater the degree of anchoring to that currency. As a special case of the model, non-anchor currency issuing countries can choose to anchor 100% to that currency, adopting a currency board regime.
In the empirical analysis, we use the augmented Frankel-Wei model to measure the degree of anchoring of the five major international currencies, including the RMB. In addition, we employ static and dynamic gravity models to investigate the impact of economic size and trade costs on anchoring effects. We find that the larger the economic size of a country, the greater the degree to which the non-anchor currency issuing countries choose to anchor to that country's currency. Furthermore, we find that a significant reduction in trade costs markedly enhances anchoring effects. Further analysis indicates that the influence of the economic size and trade costs of anchor currency issuing countries is more pronounced for non-anchor currency issuing countries with higher trade openness, more open capital accounts, and smaller economies than for countries without these characteristics. Quantitative analysis demonstrates that relying solely on economic growth and trade liberalization is insufficient to elevate the anchoring effect of the RMB to the level of the US dollar. However, financial policies represented by RMB bilateral currency swap agreements can considerably promote the anchoring effect by increasing the size of the economy and reducing trade, thus enhancing the robustness of the anchoring effect for the RMB.
We offer the following policy implications. First, it is crucial to establish a new development pattern focused on domestic circulation to enable the economy to increase in size and thus to enhance the attractiveness of the RMB to other economies. Second, optimizing external circulation involves measures such as reducing tariffs, improving trade infrastructure, and advancing the establishment of free trade pilot zones to reduce trade costs. This will further enhance the anchoring effect of the RMB. Third, the government should promote the RMB as the anchor currency for neighboring countries and then expand the anchoring role of the RMB worldwide. Finally, a high level of financial openness plays a key role in fully unleashing the potential of the RMB as an anchor currency. For instance, bilateral currency swaps can significantly enhance the potential anchoring effect of the RMB.

Employment is essential for individuals' livelihoods and directly affects economic and social factors such as income, consumption, and production. In addition, it serves as a crucial metric for evaluating high-quality development and collective prosperity. Over the past decade, China has rapidly embraced digital finance, becoming a global leader in this area. This advancement has digitally transformed the impact of technological progress on the labor market and broadened access to financial services to a wider demographic, changing labor market dynamics by lowering entry barriers to traditional financial services. However, there is a significant gap in the literature regarding the impact of digital finance development on online recruitment demands.
The rapid growth of digital finance in China provides a good opportunity to study its impact on online recruitment demands. Theoretical considerations suggest that the development of digital finance has a dual effect on online recruitment demands. It may reduce demands for and lead to the elimination of certain positions (known as the “job substitution effect”), but it also has the potential to create new job requirements (the “job creation effect”) and to indirectly stimulate entrepreneurial activities (the “entrepreneurship-driven employment effect”), thereby increasing online recruitment demands. In this paper, we use a dataset of 20 million online job postings to create indicators for online recruitment demands and assess the development of digital finance using the Peking University Digital Inclusion Index of China. We systematically examine the impact of digital finance development on online recruitment demands. Our findings indicate a notably positive impact, suggesting that the development of digital finance facilitates both the “job creation effect” and the “entrepreneurship-driven employment effect.” In addition, heterogeneous results indicate that the labor demand effects of digital finance development are widely present across various educational backgrounds, experiences, and wage levels. This confirms the inclusiveness of digital finance development at the job level.
This study contributes to the literature in the following aspects. First, it highlights the considerably positive impact of digital finance development on online recruitment demands. Despite extensive scholarly attention to the economic aspects of digital finance development, focusing on macroeconomic growth, micro-level corporate financing behavior, and household entrepreneurship and consumption, there remains a notable gap in understanding its effects on online recruitment demands, a gap which our research bridges.
Second, we construct a large-scale database comprising 20 million online job postings, which exhibits excellent scalability. Using big data methodologies, we extract information from these postings as the foundational dataset for our study, which has gained prominence in recent academic research. In contrast to household surveys, our dataset not only has a more substantial sample size but also provides richer information. For instance, in testing the “job creation effect,” we use machine learning to identify digital positions based on the lexicon proposed by Chen and Srinivasan (2023). Given the advantages inherent in this dataset, we anticipate its broader application in the future.
We conduct additional tests to explore the online recruitment demand implications of digital finance development, enhancing our understanding of its interplay with traditional finance and its role in responding to challenges posed by external shocks. Our findings indicate that the labor demand effects of digital finance development are more pronounced in areas with lower (vs. higher) bank branch density, confirming a clear complementary relationship between digital finance and traditional finance. Furthermore, the development of digital finance helps mitigate the adverse effects of external shocks on online recruitment demands. These tests refine our insights into the dynamics of digital finance development.
This study has important policy implications. First, it is crucial to promote the healthy development of digital finance. Second, efforts should focus on improving the digital skills of the workforce to meet the growing demand for talent in the evolving digital finance landscape. Concurrently, greater support should be provided for entrepreneurial activities to maximize the employment effects of digital finance development. Third, initiatives should aim to encourage the development of digital finance in regions with weak traditional finance.

Since the beginning of the 21st century, China has achieved prominent success in technology and industrialization, but future innovation challenges in China have also increased, particularly in the context of increasing global technology restrictions. The need to enhance the independent development of core technologies and radical innovations is critical for China's technological progress during the period of its 14th Five-Year Plan. Concurrently, with emerging digital technologies such as big data, blockchain, cloud computing, the IoT, and AI becoming prevalent in finance, China's fintech sector has experienced rapid growth. Exploring whether fintech facilitates radical innovations and how it can drive them is crucial for leveraging innovation to drive high-quality economic development and enhance financial services for the real economy.
This paper constructs a complex network of 1.4 billion global patent citations to calculate a radical innovation index for all patents worldwide, and assesses the level of radical innovation in China's A-share listed companies for the period from 2007 to 2020 to empirically test the impact of fintech on corporate radical innovation. The results show that fintech considerably increases the level of corporate radical innovation. Financing optimization, knowledge accumulation, and digital transformation are effective channels through which fintech boosts innovation. Further analysis reveals that the primary effect of fintech is achieved through cross-border impacts, guiding non-digital firms in digital patent innovations. Finally, this study finds that fintech empowers resource-scarce and relatively weak enterprises to achieve rapid technological advancements in innovation.
This paper makes the following potential contributions. First, we precisely measure radical innovations in China's listed companies, providing a data-based foundation for empirical analysis of the impact of fintech on digital innovation, and offering insights for future research on radical innovation. Second, this paper explores the impact of fintech on innovation through its ability to ease financing constraints and foster knowledge accumulation and digital transformation. Thus, we expand the understanding of factors that influence corporate innovation and demonstrate the enabling effect of digital transformation, which can inform evidence-based government policies. Finally, from a theoretical perspective, this study examines the differential impacts of deepening and cross-border effects on innovation from the perspective of historical international patent classification categories and the digital industry. The results show that the main roles of fintech are to support cross-industry R&D and enhance digital innovation in non-digital firms.
This study's findings provide valuable insights for continuing financial supply-side reforms and optimizing innovation incentive policies to achieve pathways to radical innovation. In the new technological revolution, the Chinese government should strengthen financial regulation and infrastructure, encourage banks to use fintech to improve loan processes, alleviate information asymmetry between banks and enterprises, and improve support for small and medium-sized enterprises to achieve radical innovation. In addition, the government should establish a diverse research and innovation support system that emphasizes innovation quality over the number of patent applications, thereby enhancing government support for scientific and technological innovation. Furthermore, businesses should consider fintech as a key technology for driving radical innovations and actively promote digital transformation, integrating their resources digitally to enhance their core competitiveness. Finally, the pronounced effect of fintech in supporting innovation in resource-scarce, weak, and less economically developed regions suggests the need for government attention to regional imbalances in fintech development. This includes increased policy and resource support for the less developed regions, improving markets, cooperation, and mutual aid mechanisms, and fostering a new pattern of mutual promotion and complementary development of radical innovation across eastern, central, and western regions.

Innovation is an important driving factor that determines the sustainable economic growth of a country or region. The relationship between transportation infrastructure and innovation has become the focus of Chinese and international scholars and policymakers. However, the literature is yet to reach a consensus on the relationship between transportation infrastructure and innovation. The Chinese government has long regarded the construction of infrastructure as an important strategy to promote economic growth. When China enters the transformation stage of innovation-driven development, a key question is whether its transportation infrastructure can continue to promote innovation, especially the independent innovation of firms. Thus, the ability to maintain innovation will be an important basis for the implementation of China's infrastructure strategy in the future. This paper finds that the literature presents inconsistent conclusions on the impact of highway infrastructure on innovation. This can be explained from a new perspective based on regional heterogeneity. We explore how highway infrastructure affects innovation based on three channel mechanisms: reduced information exchange costs, reduced human capital flow costs, and reduced transportation costs. To explain the impact of highway infrastructure on innovation from a micro perspective, we use firm innovation data from the “National Enterprise Innovation Survey Database” for the period from 2008 to 2014 to test the differential impact of China's urban-level highway density on firm innovation investment and the underlying mechanism. An important empirical finding is that highway density has a significant inhibitory effect on firms' investments in innovation. Furthermore, in terms of regional heterogeneity, highway density has a significant inhibitory effect on firm innovation in cities where GDP per capita is greater than average and in provincial capital cities within the province. However, it does not have this effect in cities where GDP per capita is less than average or in non-provincial capital cities within the province. Thus, it is evident that highway infrastructure has a significant inhibitory effect on micro-enterprise innovation in areas with a relatively high level of economic development and innovative enterprises, but in areas where the economic development level is relatively backward, there is no significant inhibitory effect on firm innovation. From the perspective of potential mechanisms, highway infrastructure impedes innovation activities by reducing the costs of exchanging innovation information and by reversing the flow of innovative human capital. This is the dominant mechanism through which highway infrastructure crowds out innovation in the Chinese context. Conversely, by enabling the export of enterprises' new products, highway infrastructure creates economies of scale, which promote corporate innovation. The regional heterogeneity in the impact of China's highway infrastructure on innovation results from the regional differences in the impact of this multi-layered mechanism. Specifically, in cities with high economic development levels nationwide or within the province, highway infrastructure substantially promotes innovative outsourcing activities, but has no significant impacts on innovative human capital or new products exports. In cities with relatively backward economic development levels nationwide or within the province, highway infrastructure does not have a significant negative impact on innovative outsourcing activities, and it substantially promotes innovative human capital and exports. This mechanism has led to a new pattern of development between developed and underdeveloped regions in China. This result does not align with the Chinese government's intention that the transportation infrastructure strategy should improve regions' market competitiveness in attracting investment and developing industries. In the future, China will not only apply practical measures, such as increasing the provision of livelihood support facilities and reducing high housing and rent prices to solve the problem of crowding out innovators in China's leading cities, but also continue to improve highway infrastructure in less developed cities to connect modern road network systems within and between regions.

In recent years, the bond market has played an important role in serving the real economy, optimizing resource allocation, and supporting macroeconomic policy regulation. However, since China terminated rigid payments in 2014, bond defaults have occurred frequently. In this context, the identification of bond default risk has become a new and key issue for the capital market and economic development. At the same time, financial technology (fintech) is becoming an important method to enhance the prevention and control of financial risks. In this context, this paper proposes the use of fintech, such as big data and machine learning, to develop an early warning model for bond default risk that fits the current context. This paper systematically explores the performance of big data and machine learning models in predicting bond default risk.
In terms of data, in this paper, we examine the general enterprise bonds, enterprise bonds, medium-term notes, and commercial papers issued by China's A-share listed companies in the interbank and exchange markets. We construct a macro and micro mixed big dataset with a total of 1,245 variables, including 15 macroeconomic indicators, 70 enterprise characteristic variables, and 12 bond characteristic variables. Specifically, this paper adds macroeconomic indicators that reflect the willingness and ability of local governments to rescue enterprises subject to bond defaults. In addition, we construct enterprise characteristic variables based on six categories of indicators, including valuation and growth, investment, profit, inertia, transaction friction, and intangible assets. Furthermore, we cross-multiply macro and micro indicators to construct interactive indicators. In terms of model construction, we select 10 machine learning models, including PCA, PLS, Ridge, LASSO, ENet, SVR, RF, GBDT, XGBoost, and AdaBoost. Based on the above models, we examine an early warning model of bond default risk based on big data and machine learning, and explore the economic mechanism behind machine learning.
The empirical results show that a machine learning model can predict China's bond default risk better than the classical Altman, Merton, and credit rating models. Moreover, nonlinear machine learning models perform better. The above conclusions remain valid under the modified Diebold-Mariano statistic test. We find that machine learning can distinguish differences in bond risk more effectively than the portfolio analysis method of empirical asset pricing. In addition, we determine that the advantages of machine learning models over benchmark models increase over time, and that bond default risk prediction gradually requires an increasingly complex modeling process. Finally, this paper establishes an “improved credit rating” based on machine learning spread predictions and finds that increasing rating discrimination can improve the effectiveness of ratings.
This paper further explores the economic mechanism behind the model. First, the heterogeneity analysis finds that the machine learning model has stronger predictive ability for bonds with low ratings, long issuance maturities, and high coupon rates, issuances by non-state-owned enterprises, and bonds issued in the interbank market. Moreover, its predictive ability is stronger during periods of higher economic policy uncertainty than during other periods. Second, the variable importance analysis reveals that indicators related to valuation and growth, investment, profit, intangible assets, and bond characteristics provide good warning signals of default risk in the context of machine learning, but the role of the inertia and transaction friction indicators (other than stock liquidity) is relatively insignificant. Third, machine learning models can achieve accurate predictions through default bond identification, short-term signal identification (bond trading volume), and long-term feature identification (financing constraints, internal control), and their sensitivity to “negative information” is better than that of classical models.
The contributions of this paper are as follows. First, this paper promotes the application of big data and machine learning in finance. From a theoretical perspective, we point out that classical models lack dynamic time-varying parameters and variable diversity, and therefore proceed to empirically test the effectiveness of using machine learning models. Second, this paper expands the research perspective on bond default risk. We use the continuous variable “credit spread” to quantify bond risk, and enrich bond default risk identification from a high-dimensional perspective. Third, this paper deepens the discussion on credit rating in China's bond market and finds that improving rating discrimination can improve rating effectiveness. Based on our research, future studies could further expand the prediction indicators for bond default risk from additional perspectives, such as text analysis, and use more advanced machine learning and deep learning models to improve the accuracy of predictions of bond default risk.

As the world's second-largest onshore credit market, China's corporate bond market has become increasingly important in the global financial system. With an escalating number of bond defaults, debt renegotiation has emerged as a prevailing practice in the bond market. Debt renegotiation refers to shareholders bargaining with creditors to renegotiate debt contracts when a company is in financial distress. Although the literature on debt renegotiation examines its implications for expected stock returns and corporate yield spreads, it has remained silent on how the heterogeneity of firms' debt structures affects their incentives to negotiate strategic debt servicing. In this paper, we aim to fill this gap and examine the role of firms' debt rollover frequency in shaping the pricing implications of debt renegotiation.
When a firm is exposed to rollover maturing debt, its overall rollover costs depend on its fundamentals as well as its debt structure. Structural models that embed rollover risk typically focus on the interaction with bond market illiquidity but overlook the impact on the pricing effect of shareholders' strategic actions. By incorporating rollover risk into a structural model of strategic debt servicing, we propose a novel channel through which a firm's debt structure influences its financing costs. Given the theoretical finding that shareholders' strategic behavior directly affects corporate bond spreads by means of liquidation costs and bargaining power, the new insight offered by our model is that the rollover channel amplifies the effect of debt renegotiation on credit spreads: a higher rollover frequency forces equity holders to absorb a greater rollover loss per unit of time and, in turn, the increased cost of complying with debt obligations motivates firms to strategically service their debt at a higher fundamental threshold. It follows that bondholders increase the credit spread ex ante as compensation for the increased probability of strategic debt service.
Using the theoretical implications derived from our model, we empirically test these hypotheses using a data sample of over-the-counter market transactions of corporate bonds issued by Chinese public firms from May 2014 to December 2020. In the baseline regression analysis, we consider two proxies for liquidation costs—the concentration of an issuer's industry and the degree of tangibility of its assets—and measure shareholders' bargaining power by the fractions of equity owned by the firm's CEO as well as the proportion of public debt to its total debt financing. We find that credit spreads widen as the liquidation costs and bargaining power of shareholders increase, regardless of the empirical proxies used. This finding identifies debt renegotiation as an important determinant of corporate yield spreads in China.
Furthermore, we find evidence that rollover risk is an accelerator in the pricing impact of debt renegotiation. Specifically, the effect of debt renegotiation on credit spreads is more pronounced for bond issuers with a higher (vs. lower) proportion of long-term debt maturing within a year, which we use as a proxy for rollover risk exposure. This accelerator effect is statistically and economically significant after controlling for other well-documented determinants of corporate bond spreads.
In addition, our heterogeneity analysis shows that the impact of rollover risk on the renegotiation effect is stronger for bonds with a short time to maturity, small firms, and firms in financial distress than for other firms. Moreover, our results remain robust to alternative measures of credit spreads, alternative proxies for debt renegotiation risk and rollover exposure, and the exclusion of bonds with option-like features. Finally, we introduce instruments with respect to our strategic proxies and confirm that our main results are unlikely to be driven by potential endogeneity effects associated with these proxies.
This paper makes three contributions to the literature. First, we extend the strategic debt service model by incorporating firms' exposure to rollover risk. As such, our study lays a solid theoretical foundation for subsequent research. Second, this paper enriches the literature on the determinants of credit spreads by testing the effect of liquidation costs and bargaining power in the Chinese corporate bond market. Our empirical results contrast with findings obtained from developed credit markets. Finally, we empirically test the marginal effect of debt renegotiation through a rollover risk channel. Our findings demonstrate the role of rollover risk in shaping credit spreads above and beyond its interaction with debt illiquidity.

Given the important role of credit ratings in mitigating information asymmetry, regulation relies on such ratings, as reflected in regulatory conditions being set based on credit ratings in the bond issuance process. This is manifested in three aspects of the process: first, the issuer needs to provide a credit rating in the bond issuance process; second, the success of bond issuance depends on the credit rating; and third, the issuer is required to provide simultaneously both issuer and facility ratings. Although the regulation requiring credit ratings as a criteria for bond issuance aims to improve issuance quality, it raises a series of issues. First, to meet the requirements for bond issuance, credit ratings are concentrated at specific thresholds. For example, because the regulatory requirement for bond issuance is that the credit rating is not lower than AA, the majority of bonds in the Chinese market are rated AA and above. This is problematic because it leads to insufficient differentiation in credit ratings, which cannot accurately reflect the default risk of issuers. In addition, it can result in credit rating inflation, reducing the quality of credit ratings. The aim of disclosing facility credit ratings and guarantees is not to provide incremental information but to enable issuers to enhance their credit to achieve a higher rating if their initial rating is insufficient, and thus to improve the overall credit rating and increase the success rate of bond issuance. The People's Bank of China clearly pointed out in December 2020 that “problems such as false high ratings, insufficient differentiation and weak pre-warning function have restricted the high-quality development of China's bond market.”
In response to these issues, the credit rating regulation policy was adjusted in 2021. On February 26, 2021, the “Management Measures for the Issuance and Trading of Corporate Bonds” were revised, abolishing the mandatory provisions on credit ratings for public issuance of corporate bonds. On March 26, 2021, the “Notice on the Implementation of Arrangements for Canceling Compulsory Ratings of Debt Financing Instruments” pointed out that in the issuance process, the mandatory disclosure requirements for facility rating reports would be canceled, and the disclosure requirements for issuer rating reports would be retained. On August 11, 2021, the People's Bank of China decided to pilot the cancellation of the credit rating requirements for the issuance of non-financial enterprise debt financing instruments. The main purpose of the policy canceling the mandatory facility rating in the issuance process is to change the disclosure of facility credit ratings from mandatory to voluntary. Thus, the policy removes the regulatory role in facility credit ratings but, by retaining issuers' credit ratings, it enables the voluntary disclosure of facility credit ratings to provide incremental information.
This paper finds that after the implementation of the mandatory rating policy, the quality of ratings of issuers that retain the facility rating declines, and credit rating quality decreasing is lower when the reputation of the rating agency is high. The heterogeneity analysis shows that the above relationship mainly holds when there is a high degree of competition among rating agencies, the level of customer importance is high, bond issuance is the issuer's initial issuance. Finally, this paper shows that after the implementation of the mandatory facility rating policy, for issuers that retain facility ratings, the inflated credit rating reduces the financing cost of bonds, indicating that the bond market is not fully aware of credit rating inflation, prompting issuers to seek inflated credit ratings.
The results of the paper indicate that, on the one hand, the abolition of the mandatory facility rating policy enhances the degree of competition among rating agencies and the motivation of rating agencies to cater to issuers, and thus reduces the quality of credit ratings in the short term. On the other hand, once the selection of a facility rating is made voluntary, the reputation mechanism of the rating market is conducive to a “market-driven” improvement in credit rating quality in the long run.

The insufficient endogenous capacity caused by factor shortages has become a key issue restricting sustainable rural development, and the backwardness of rural industrial development is an important reason why rural areas are in a state of long-term net outflows due to the difficulty in meeting the flow preferences of capital and labor factors. Given the important role of industries and factors in rural revitalization, the central government clearly states in work reports and other documents that guiding capital and labor resources to flow to rural areas is essential for the development of agriculture, rural areas, and farmers. The central government emphasizes that industrial prosperity is an important foundation for achieving rural revitalization, and proposes to achieve this by enhancing the attractiveness of rural industries and promoting their development. Given the increasingly important role of rural e-commerce in poverty alleviation, the Ministry of Finance and the Ministry of Commerce jointly launched a Comprehensive Demonstration Policy of E-commerce into Rural Areas in 2014, to enable more regions to share the digital dividends brought by e-commerce development, and have achieved positive results. However, the shortage of capital and labor factors is restricting the development and effectiveness of rural e-commerce and is an urgent problem requiring resolution.
In the dual context of the continuous release of digital dividends in rural e-commerce and the serious constraint of factor shortages on rural development, an important issue requiring attention in the rural revitalization process is whether the development of e-commerce in rural areas can slow the outflow of factors, provide industrial support for rural development, and guarantee the availability of production factors, such as funds and labor. This article regards the Comprehensive Demonstration Policy of E-commerce into Rural Areas as a quasi-natural experiment, adopts a difference-in-differences (DID) method, and uses national county-level panel data. This approach ensures a large sample size, incorporates both capital and labor factors into the research framework, and reduces endogeneity. In addition, the study examines the impact and mechanism of rural e-commerce policies on factor flows from multiple perspectives to accurately depict the general impact of digital technology, represented by rural e-commerce, on the flow of factors in empowering rural industrial development at the national level.
Specifically, this study examines the impact of the Comprehensive Demonstration Policy of E-commerce into Rural Areas on the flow of rural capital and labor factors using the DID method and national county-level panel data for the period from 2011 to 2019. The results show that the implementation of this policy considerably reduces the per capita capital outflow from rural areas, attracts population inflows, effectively improves the factor attractiveness of the demonstration counties, and provides an industrial foundation and factor guarantee for rural revitalization. A mechanism analysis reveals that rural e-commerce policies significantly reduce the outflow of rural factors by promoting rural entrepreneurship, reducing the urban-rural income gap, and increasing factor inputs. Further research demonstrates that the expenditure structure of national policies supporting the development of rural e-commerce and differences in distance between regions and their prefecture-level cities have heterogeneous effects on the flow of factors in demonstration counties. When the level of digital technology development in regions is low, the implementation of rural e-commerce policies can significantly slow capital outflows, whereas when the level of digital technology development is high, this implementation attracts considerable population inflows. In addition, the increase in the number and proportion of demonstration counties in each city creates a siphon effect, with capital flowing from demonstration counties to non-demonstration counties, thereby creating a spillover effect on their population flows.
To better leverage the effectiveness of rural e-commerce policies, activate the potential of rural factors, and promote rural development, various regions should fully leverage the collaborative role of the government, digital technology, and farmers, continuously strengthen policy guidance and financial support, improve the efficiency of financial fund utilization, continuously improve the rural e-commerce ecosystem, and optimize the developmental environment of the e-commerce industry. In addition, regions should leverage government and digital technology to enrich the rural industrial ecosystem, improve the urban-rural factor exchange system, slow the outflow of rural factors, and guide them to serve the development of agriculture, rural areas, and farmers by providing institutional guarantees, industrial support, and factor support for rural revitalization.

China's personal income tax system was not established for a single revenue-raising function but to regulate the distribution of income, which contrasts strongly with the tax systems of other major countries, such as the US, where personal income tax accounts for a relatively high proportion of total tax revenue. The main focus of China's personal income tax system is to change the degree of progressive taxation by adjusting the basic expense deduction standard, and thereby affecting the tax burden on individuals' wages and salaries and the macro distribution of income. The standard for basic expense deductions has long attracted the attention of academics and the public. A lower standard for basic deductions reduces both the motivation of individual residents to work and the effect of personal tax in regulating the macro distribution of Chinese residents' income. Therefore, how to preserve the motivations of individual residents to work while also narrowing the social income distribution gap is a challenging issue for personal income tax reform. Currently, most of the literature examining how the basic deduction standard affects personal welfare, such as residents' income, employment, and consumption, is based on simulations of theoretical models. Only a small number of scholars use micro data to analyze how changes in basic deduction standards affect individual consumption behavior, and there is room for further improvement in research design and the accuracy of income distribution and tax data.
As noted, the basic deduction standard in China's personal income tax system is established to prioritize income distribution adjustments. Since the basic deduction standard was first set in 1980, it has been revised seven times to date. The largest reform of the personal income tax system in the past 20 years occurred in September 2011, with the increase in the basic personal income tax deduction standard from RMB2,000 to RMB3,500. This reform, which had important implications, provides a good basis for our research. This paper uses monthly bookkeeping data from China's Urban Household Survey (UHS) for the period from 2010 to 2012, and the 2011 policy that increased the basic personal income tax deduction to construct an individual-year-month difference-in-differences (DID) model to assess how the adjustment of deduction affects both the tax burden on individuals' salaries and wages at the micro level and the macro-level income distribution.
The conclusions of this paper are as follows. First, the individual income tax reform prompted a reduction in the individual tax burden of about 36.31%, with individuals earning 2,000-16,100 yuan per month being most affected by the adjustment of the expense deduction standard, and groups with higher incomes being mostly affected by the adjustment of the tax bracket. Second, although the tax reform reduced the personal income tax burden, its regulatory effect on the social income distribution at the macro level was not strengthened; indeed, it was weakened to an extent. Third, the increase in individual after-tax income following the tax reform had a limited impact on labor supply, but it improved basic consumption for low-income groups and promoted service consumption expenditures of high-income groups.
The contributions of this article are as follows. First, we enrich the literature by providing a more detailed research design than is traditionally used. As personal income tax was paid on a monthly basis around 2011, the most appropriate way to capture the impact of the personal income tax reform is to conduct research on a monthly basis. Therefore, unlike the traditional individual-year DID model, we construct an individual-year-month DID model to better evaluate the impact of policy changes. Second, we determine a more accurate income distribution than has been determined to date. This article confirms that the difference between the income distribution at the theoretical and practical levels is small, and provides evidence that will support future theoretical analyses and empirical research. Third, we provide more detailed metrics than other studies. This paper uses detailed micro data for policy evaluation and removes the impact of two individual income tax reforms that occurred in the same year. The unique monthly bookkeeping data from China's UHS, which include information on income, expenditure, tax payments, and employment at the individual monthly level, provide a sound basis for assessing the impact of the change in the standard for the basic deduction of personal income tax in 2011 on the actual tax burden of individuals and the distribution of income in society.

Corporate culture is widely believed to have a significant impact on an organization's performance. It plays a crucial role in shaping corporate identity and improving business performance. Corporate culture encompasses shared values, beliefs, and behaviors that define appropriate behavior by employees. Recent studies show that corporate culture increases resilience to stock price changes and enhances firm performance in crises. Considering the importance of corporate culture, we seek to investigate corporate culture in China and its financial and economic outcomes.
However, corporate culture is subjective and nebulous and lacks an authoritative and convincing definition. Fortunately, the development of big data and machine learning algorithms makes identifying and measuring the strength of corporate culture possible. Using the k-means model, we identify the five most mentioned values from the descriptions of firm values on firms' official webpages and use them to define corporate culture, namely, “integrity,” “innovation,” “hardworking,” “quality,” and “teamwork.” Next, we use the word embedding model to create dictionaries for these values and quantify the strength of corporate culture using annual report textual data.
Financial constraints are a key factor hindering Chinese enterprises' development. Informal financing sources such as trade credit are effective supplementary systems that can alleviate financing issues. Considering the high dependence, informational opacity, and weak regulation that characterize trade credit in China, we focus on how corporate culture influences access to informal financing. We propose that corporate culture is an important strategic “soft asset” for firms and an effective complement to other characteristics in helping firms obtain trade credit.
We propose three channels through which corporate culture influences trade credit access. First, the “integrity” and “hardworking” corporate culture values advocate that employees abide by contracts and commitments, fulfill their obligations, and thereby earn respect, enhance their reputation, and gain trust from creditors. Second, the “innovation” and “teamwork” values emphasize long-term development strategies and induce suppliers or customers to maintain good relationships by providing trade credit, aiming to share business growth in the future. Finally, companies that attach considerable importance to product quality have a strong cultural emphasis on the “quality” value and are inclined to use trade credit to supervise the quality of suppliers' products, thereby improving the overall level of trade credit.
In empirical analyses, we focus on data from Chinese A-share listed companies over the 2012-2021 period. We collect textual data from the Management's Discussion and Analysis section of companies' annual reports and obtain financial data from the China Stock Market and Accounting Research database.
The results of our empirical analyses demonstrate that a strong corporate culture helps companies obtain more trade credit than those lacking such a culture, and that all five dimensions contribute to this effect. This conclusion holds after a series of robustness checks. For the endogeneity issue, we use a two-stage least squares model with two instrumental variables. In a difference-in-differences regression, we use the abnormal departure of the chairperson of the board or CEO as an exogenous shock in a quasi-natural experiment. The results confirm our main findings.
The results of the mechanism analyses indicate that companies with stronger corporate cultures have lower levels of credit risk and higher potential for future development than companies with weaker cultures. In addition, these companies pay more attention to product quality and rely more on trade credit to supervise the quality of their suppliers' products than their counterparts. The results of further analysis show that the effect of corporate culture is more significant in firms with lower information transparency and those located in regions with lower social trust levels and poorer business environments than in other firms.
This article makes three main contributions. First, it introduces a method for measuring the strength of corporate culture in the Chinese setting. Second, by accommodating the unique characteristics of Chinese firms, this article explores the cultural dimensions of most concern for listed companies, enabling a comprehensive study of corporate culture in China. Third, this research enriches the literature on the factors that influence access to trade credit and provides evidence of the positive role of a strong corporate culture in obtaining trade credit, thus offering a new perspective on how companies can alleviate financing constraints.