Intensive regional competition for investment attraction and economic development has been one of the most salient features of China's economic miracle in the past decades. As one of mainstream analytical approaches to analyzing Chinese local governments and bureaucrats, the promotion tournament theory has attracted increasing attention from international and domestic scholars, and greatly enhanced the research frontiers about bureaucratic incentives, government behavior, and the China's economy. This article provides a comprehensive review of theoretical and empirical studies along this line of research, assesses their contributions to understanding the institutional logic of China's reforms and opening policies and economic development, and clarifies some common misunderstandings and confusions about this approach. This article also evaluates the limitations and problems of the existing literature and suggests future directions of research.

In the modern international monetary system,only very few international currencies with the function of storing value and as the medium of exchange are the central bank currencies (notes and coins),and most are government bonds and commercial bank deposits of the countries that issue international currency.From 1976 to 2022,foreign holdings of U.S.financial assets exhibited the following characteristics.The largest proportion of foreign official holdings of U.S.securities are U.S.government bonds.Most foreign official holdings of U.S.government bonds are treasury bonds.The largest proportion of foreign private holdings of U.S.securities are U.S.corporate bonds and stock other than Treasury securities.The second is U.S.liabilities reported by U.S.banks and securities brokers.The third is U.S.liabilities to unaffiliated foreigners reported by U.S.non-banking concerns.The fourth is U.S.Treasury bonds.

In a closed economy,the government can obtain government bonds seigniorage,and commercial bank can obtain commercial banks seigniorage.In an open economy,the governments of countries that issue international currencies can gain government bonds seigniorage from foreign-owned government bonds,referred to as government bonds international seigniorage,and commercial banks can gain seigniorage from foreign-owned deposits,referred to as commercial bank international seigniorage (Canzoneri et al.,2013; Cova et al.,2013).Therefore,international seigniorage gained by countries that issue international currencies includes central bank international seigniorage,government bonds international seigniorage,and commercial banks international seigniorage.

The Federal Reserve Banks do not pay interest on Federal Reserve bank notes in circulation outside the U.S.The international seigniorage gained by the Federal Reserve is calculated with the opportunity cost of seigniorage every year,i.e.,the market yield of U.S.Treasury securities at 1-year maturity.The outstanding stock of Federal Reserve bank notes outside U.S.comes from the International Investment Position (IIP) of the United States.

Government bonds international seigniorage.Foreign holders of large amounts of U.S.government bonds used for storing value both increased the price of U.S.government bonds and reduced the cost of financing (Greenspan,2005; Craine and Martin,2009; Francis and Warnock,2009; Krishnamurthy and Vissing-Jorgensen,2012).Government bonds international seigniorage is the total of the interest expense saved from financing in international financial markets at a low cost by the government.The categories of foreign-owned U.S.government bonds are very diverse,and the detailed statistics of the balance every year are difficult to gather.Here the data on foreign-owned U.S.government bonds every year (including treasury bonds and other government bonds) comes from the International Investment Position (IIP).Some reports have presented a numerical value for the cost of financing saved by U.S.government bonds (Greenspan,2005; Craine and Martin,2009; Francis and Warnovk,2009).However,the level of interest rates of U.S.government bonds is either high or low in different periods.The cost of financing saved may be underestimated in a period with a high interest rate,and overestimated in a  period with a low interest rate.In order to overcome this deficiency,a relatively precise way has been adopted in this paper,where the cost of financing saved by U.S.government bonds account for 10 percent of market yield on U.S.Treasury securities at a 1-year constant maturity.In normal circumstances,market yield on U.S.Treasury securities at a 1-year constant maturity is around 5 percent,and the cost of financing saved is about 50 p.m.,which is relatively close to the empirical analysis results (Greenspan,2005; Craine and Martin,2009; Francis and Warnock,2009).

Commercial bank international seigniorage.In the dollar-dominated international monetary system,foreign holding of commercial banks deposits reduces the financing costs of U.S.commercial banks.International seigniorage gained by U.S.commercial banks is the interest expense saved from the international market due to lower financing cost.It equals the amount of foreign-owned liabilities of commercial banks,multiplied by the savings in financing cost.In this paper,the data of foreign-owned liabilities of commercial banks come from the IIP statistics of U.S.liabilities reported by U.S.banks and securities brokers.Following the same reasoning above,the financing cost saved by U.S.commercial banks equals about 12% of the market yield on U.S.Treasury securities with a 1-year maturity.

The international seigniorage gained by the U.S.includes international seigniorage gained by the Federal Reserve,government bonds international seigniorage,and commercial bank international seigniorage.The proportions of the three components had small variations but remained roughly equal during 1976 to 2013.First,the international seigniorage gained by commercial banks is the largest component,with the mean,the maximum,and the minimum at 37.45%,47.81%,and 27.05%,respectively.The second largest is the international seigniorage gained by the Federal Reserve with the mean,the maximum,and the minimum at 34.47%,44.44%,and 24.28%,respectively.The least is the government bonds international seigniorage with the mean,the maximum,and the minimum at 28.67%,40.38%,and 22.80%,respectively.

Analysis of the international seigniorage gained by the U.S.shows that the central bank’s international seigniorage is only a part of the international seigniorage.Commercial bank international seigniorage and government bonds international seigniorage are equally important.

In the realm of international politics and diplomacy,economic sanctions have emerged as a non-military coercive tool that is witnessing a notable surge in utilization. Statistical data compiled by Felbermayr et al. (2020) reveals that the span from 2016 to 2019 alone witnessed a total of 75 instances of unilateral,bilateral,and multilateral sanctions. Recent years have witnessed a confluence of events,including the China-United States trade war,Brexit,the Covid-19 pandemic,and the Russia-Ukraine conflict,which collectively led to a stagnation,and at times,even a reversal of the globalization trend,thereby fostering an intensified climate of “de-globalization”. Amid this evolving landscape,the prospect of countries employing economic sanctions to accomplish specific objectives has been on the ascendant.The critical juncture of February 24,2022,saw Putin's official announcement of the initiation of special military operations against Ukraine,coinciding with Zelensky's declaration of Ukraine's full-scale engagement in a state of war. This Russia-Ukraine conflict has engendered two immediate consequences. Firstly,a rapid escalation in global geopolitical risks. The conflict swiftly evolved into Europe's most substantial military confrontation since the culmination of World War II. The uncertain trajectory of these hostilities,coupled with the ominous specter of their escalation and geographic expansion,has propelled global geopolitical risks to unprecedented heights. This elevation of risk is underscored by the IMF's World Economic Outlook for Q1 2022,wherein the term “war” resounds a staggering 122 times. Secondly,the conflict has catalyzed the imposition of economic sanctions of an unprecedented magnitude. Following the outbreak of hostilities,the United States,the European Union,and other relevant parties have imposed far-reaching economic sanctions upon Russia. Noteworthy for their comprehensive utilization of diverse sanctions instruments and influenced by the distinct political and economic positioning of the targeted nations,these sanctions bear unique historical attributes.

This study,which focuses on the Russia-Ukraine conflict,adopts an innovative approach that employs the high-frequency decomposition method,encompassing pre-event trends. It seeks to uncover the spillover effects of economic sanctions upon various countries'stock and foreign exchange markets. Specifically,the research amalgamates the temporal evolution and risk perception features of geopolitical conflict occurrences to dissect the spillover effects of both geopolitical risks and economic sanctions from the rapid fluctuations in stock prices and exchange rates across diverse nations. The findings indicate that economic sanctions against Russia have precipitated heightened volatility in the global stock and foreign exchange markets,with noteworthy variations in the responses of distinct countries.

Subsequently,this study delves into an exploration of the heterogeneous responses among different nations by introducing an array of country-specific characteristic variables. The outcomes shed light on the fact that countries with higher levels of cross-border investment from Russia exhibit a more pronounced negative impact on their stock markets. Similarly,nations that maintain significant imports from Russia experience more considerable currency depreciation. In essence,nations reliant on Russian goods and capital “imports”,as opposed to those engaged in “exports” to Russia,bear more pronounced negative repercussions. This heterogeneity stems from the substantial scale of Russia's investments in foreign countries,surpassing overseas investments in its own nation. Consequently,the freezing of overseas assets swiftly erodes the liquidity and valuation of these assets. Additionally,as an export-oriented economy,Russia's impact is particularly profound on countries heavily dependent on its energy and primary commodity imports.

Further analysis underscores that extensive economic sanctions against Russia could substantially elevate global commodity prices,thereby amplifying the risk of stagflation within the global economy. Furthermore,the spillover effects of these economic sanctions could also reshape the global supply chain paradigm to a certain extent. Notably,nations with a strong reliance on Russia's energy exports would confront heightened inflationary pressures,while energy-producing nations would benefit from substitution effects,boosting their energy exports and strengthening their global supply chain positioning.

The contributions of this study are manifold. Primarily,it provides a cohesive research framework encompassing countries worldwide,aiming to examine the broader global spillover ramifications of economic sanctions. This departs from earlier literature which often focused on the effects of sanctions in isolation on either the sanctioned or the sanctioning nations,overlooking the extensive extraterritorial consequences under the context of economic integration and financial globalization. A study by Kwon et al. (2022) scrutinized the impact of US economic sanctions against Cuba on other nations' trade status during that year. However,the Cuban economy's limited size and low-frequency data have prompted debates about the strict causal connection between the two variables. In contrast,this study undertakes a more robust exploration of the “economic sanctions spillover effects” through a comprehensive analysis of the widespread sanctions against Russia.

Secondly,the study provides economic rationales to elucidate the differential responses observed in stock and foreign exchange markets across various nations. It complements existing research by delving into potential implications of economic sanctions on global stagflation and supply chain restructuring. By delving into the causes of heterogeneous spillover effects from economic sanctions through the lens of national characteristics,the paper unravels the intrinsic logic while interconnecting it with the sanctions' toolkit utilization and Russia's unique economic and financial structure. Moreover,it assesses the plausible repercussions of economic sanctions on the broader global context,contributing to an enriched research scope.

Lastly,the study boasts wide-ranging practical implications and applicability. Through its innovative utilization of the high-frequency decomposition method,encompassing ex-ante trends,it effectively isolates the distinct impacts of geopolitical risks and economic sanctions. This methodological framework lays the groundwork for subsequent research endeavors,offering valuable indicators for forthcoming investigations in this domain.

There has been a clear distinction between macro and micro domains in management research for a long time.This artificial division promotes academic specialization and communication among peers.However,the real firm gathers the influence of both macro and micro aspects.Both aspects are different and interrelated,constitute the complex real world,and have an interactive or collaborative influence in the historical process and frontier development.Therefore,it is difficult to understand the mechanism from a single perspective accurately (Zhang and Zhang,2014a,2014b),and more and more important problems need to be analyzed and solved by comprehensively utilizing the advantages of both macro and micro perspectives (Huselid and Becker,2011).Therefore,connecting the macro and micro fields will help open up new research methods and theoretical innovation paths and bridge the gap between science and practice (Aguinis et al.,2011).

In order to guide the academic community to carry out the exploration combining macro and micro fields,Coleman (1990) constructed a closed-loop model containing four lines,namely,the macro-micro “situation mechanism”,the micro-level “behavior formation mechanism”,the micro-level “transformation mechanism”,and the macro-level interaction.In March 2011,the Journal of Management published a special issue on “Connecting Macro and Micro Fields”,summarizing the challenges of integrating macro and micro research methods and theories (Aguinis et al.,2011).The special issue provides detailed guidance for research on Organizational Behavior and Human Resource Management (Huselid and Becker,2011; Joshi et al.,2011),Strategy and Policy Analysis (Priem et al.,2011),Organizational Theory (Rousseau,2011; Salvato and Rerup,2011),Corporate Governance (Priem et al.,2011),Entrepreneurial Management (Shepherd,2011),and even management research methods (Mathieu and Chen,2011).

Almost simultaneously,a group of insightful scholars in China have organized an academic seminar on “Macroeconomic Policies and Micro-Enterprise Behaviors” every year since 2012 (Rao et al.,2013),which has led great progress in domestic empirical research on the combination of macro and micro.Over the past decade,the research field of macro and micro integration in the context of China has demonstrated strong vitality due to its important academic value and policy significance,and a considerable amount of high-quality empirical literature has been accumulated.

However,the existing research in China and abroad still has much room for improvement.For example,there is too much emphasis on the impact of macroeconomic policies and not enough on the potential impact of important macro factors such as culture and technology.Moreover,as Cowen et al.(2022) pointed out,there are three common theoretical challenges in existing macro and micro studies:misuse of micro theory in exploring macro phenomena; lack of more detailed consideration of macro background in the cross-level research that focuses on micro phenomena; and less attention is paid to the polymerization process that connects the two levels.

To advance the relevant exploration,we have systematically reviewed empirical studies published in three Chinese economics and management journals since 2012,namely Journal of Management World,Economic Research Journal,and Social Sciences in China,which examine both macro environment and micro enterprise behavior.In this paper,we seek to answer two questions:①In the context of China,what are the macro factors that have an impact on micro factors in existing studies that combine macro and micro perspectives? ②Based on these findings,which directions are worth exploring in the future?

Drawing on the PESTEL analysis model (Doyle,2016),this paper summarizes the macro-environment that affects the behavior of enterprises into six elements:Political,Economic,Social,Technological,Environmental and Legal.Furthermore,through investigating the interactions of macro and micro under three different circumstances:macro environment as independent variable,moderating variable (top-down,“trickle-down effect”),and dependent variable (bottom-up,“pop-up effect”),we clarify specific macro and micro factors.

Based on the above analysis of existing research deficiencies,this paper proposes future research directions in four major areas.Firstly,researchers should expand data sources from archival data to survey data and update measurement indicators of the institutional environment.For example,researchers can use business environment indexes including formal and informal indicators developed by Zhang and Zhang (2023) at the province level,and Zhang et al.(2023) at the city level in Chinese mainland.Furthermore,researchers should improve analysis methods,such as selecting a variety of analysis methods to verify each other,paying attention to robustness tests,and further tracking and using breakpoint regression or case study analysis methods to ensure the reliability of research results.Secondly,researchers should investigate the configuration effect to identify what macro-environmental factors are more important and how they depend on each other to influence micro-corporate behavior.Moreover,researchers should promote the integration of theories,such as institutional theory and upper echelons theory,and explore the micro influence on the macro.Thirdly,researchers should pay attention to mediating effects,emphasize moderating effects,and explore mixed effects.Finally,researchers should conduct Sino-foreign dialogue based on China's practice and consider the international environment.

As China’s economic growth is set to depend more on innovation and less on the growth of factors,increasing total factor productivity (TFP) becomes more important and urgent for the realization of high-quality development in the new development stage.The paper aims to estimate the growth rate of China’s TFP and its contribution rate to economic growth,and compare them with the U.S.economy.We use the method of Tornqvist index in the growth accounting framework,following the practices conducted by OECD,BLS and PWT.To accurately estimate TFP,we instead use the capital services,other than capital stock,to measure capital input,and also use the time spent,other than employment,to measure labor input.We also investigate the role of heterogeneity of labor input and various measures of labor income share in TFP estimation.

The papers focus on the period from 1997 to 2021 because of the availability and the quality of data.The time span of twenty-five years can reveal the long-run trend of China’s TFP.We find that the growth rate and the contribution rate of China’s TFP rose constantly before 2007 and showed  a V-shaped trend after.The growth rates in 2010,2011 and 2020 were among the lowest,due to the global financial crisis and the shock of Covid-19.The contribution rate recovered rapidly after 2012,and was mostly larger than that in 2006 after 2015.The growth rate of China’s TFP was higher than the U.S.in most  years,but their gap narrowed after the crisis.The contribution rate of China’s TFP to growth was lower than the U.S.before the crisis,then rose to surpass the U.S.in most of years after the crisis.The differences in the trends of TFP growth between China and the U.S.can be largely attributed to the differences in development stages of technology,innovation and structural transformation.Because the growth dividends from the scientific and industrial revolution were released earlier in the U.S.,and the rise of services with lower TFP growth was more salient in the U.S.,the TFP growth in the U.S.declined significantly.Moreover,China’s growth dividend from market-oriented reform and R&D investment was also an important contributor,as it increased the efficiency of factor allocation and utilization.

To achieve steady TFP growth,China should continue the market-oriented reform and high-level opening,and build modern industrial system supported by the real economy.First,deepening market-oriented reform could release the reform dividend for growth.We suggest China should further reform its factor markets,especially in labor market,land market and capital market,to optimize factor allocations between sectors and regions to increase factor efficiency.Second,accelerating high-level opening could enhance China’s comparative advantage in the global supply chain.We suggest China should further advocate a new wave of globalization to gain more growth momentum,and steadily open up its financial market,services and digital economy to draw more foreign investors.Third,building modern industrial system supported by the real economy could spur the process of innovation and technological change.We suggest China should stabilize the share of manufacturing,seize the opportunities presented by the new round of industrial revolution,enhance the technological innovation capacity,and promote changes in the whole industrial systemin terms of quality,efficiency and driving force.

The paper conducts a more accurate and rigorous empirical study on the estimation of China’s productivity growth,which offers more facts and evidence for China to recognize its trends of economic growth and structural transformation,and also derives corresponding policy implications for China to better promote high-quality economic development.

Big data and relevant technologies have not only provided unprecedented amounts of data related to the macroeconomy and the whole society,formalizing a big data “ecology”,but also influenced and reshaped the process of public policy making and operation.Meanwhile,intensive attention from the field of economic research,particularly from the perspective of central banks all over the world,has been paid to big data and relevant analytical methodologies.

We know that the main functions of a central bank are,within the framework of a country's monetary policy operations,to use conventional monetary policy tools (such as open market operations,discount-window loans,and required reserve ratios),or unconventional monetary policy tools,to adjust interest rates and money supply,to achieve the mandates of monetary policy,such as full employment and price stability.In different stages of monetary policy operations,including before,during,and after,the central bank's daily work includes:collecting a large amount of data,conducting regular data analysis,macroeconomic forecasting,and economic cycle analysis; releasing regular monetary policy reports and communicating with the public (traditional press conferences along with widespread use of social media such as Twitter and Facebook overseas,and Weibo and WeChat in China); and conducting micro-financial supervision and macro-prudential supervision based on a large amount of financial data,and so on.It's worth noting that,especially after the 2008 Great Recession,central banks around the world have paid more attention to macro-prudential supervision,closely monitoring real-time dynamics in specific financial markets,such as shadow banking,systemically important financial institutions,and the real estate market,through big data analysis.Therefore,in the current  big data era,from the perspective of central banks,we want to address the following questions through a review of the literature:With the emergence of big data and related technologies,what new changes have occurred in data collection and analysis by central banks,particularly in the field of macro finance research and analysis,and in which specific areas of macro finance?Alongside new granular micro financial data and new analytical tools,what interesting new predictions and analysis results have emerged?Have new applications arisen in the fields of monetary policy communication,macroeconomic forecasting,and macro-prudential supervision?In comparison to traditional data and analytical methods,what advantages do big data analytics have,and has it also brought new problems,risks,and challenges?

This paper conducts a comprehensive review of recent heuristic efforts in applying big data analytics to macro finance,offering contributions of review as follows.Firstly,the review focuses on a central bank's perspective.Secondly,it covers diverse data types such as textual data and emerging economic indicators (e.g.,electronic payments,mobile data,satellite images).Thirdly,it employs varied analytics like Bayesian dynamic factor models for real-time economic trend estimation.Lastly,it provides insightful suggestions for future research and application,particularly concerning China.The review identifies three key literature domains.First,researchers extract structural insights from various central bank communication channels,including press releases and media sentiment.Second,big data enables more accurate macroeconomic forecasting,even narrowing the gap between the current and most recent data (nowcasting).Third,big data bolsters macro-prudential policies by offering indicators for policy framework enhancement,supervision,crisis prediction,and market trends.While big data's potential is acknowledged,unexplored avenues persist,especially in China.Recommendations include analyzing media sentiment regarding monetary policies,leveraging China's data-rich environment for better nowcasting,and exploring central bank digital currency (CBDC) applications in big financial data collection and analysis.

Using China's 2020 population census combined with POI and commuting data,this paper measures the population size within the spatial boundary of urban core area for each of the prefecture level cities in China.We measure the corresponding GDP of urban core area with the help of nightlight data.We then investigate the relationship between cities' GDP per capita and population size thus defined.Following Au and Henderson (2006) and Baum-Snow et al.(2017),we use cities' historic social-economic conditions and Bartik measure as IVs.We find there exists a significant and robust inverted-U relationship between a city's GDP per capita and its population size.

Furthermore,such an inverted-U relationship varies with both cities' economic structure and spatial structure,which means that each city has a unique optimal size of population under which the city's GDP per capita reaches maximum given its current economic structure and spatial structure.Specifically,our empirical findings suggest that if a city has a higher share of tertiary industry relative to secondary industry,or if it has a larger R&D output captured by patent value,the city tends to have a  larger optimal size of population because such a city has greater potential of agglomeration economies.On the other hand,if a city has a bad spatial structure,then the city tends to have a  smaller optimal size of population because such a city is more likely to suffer from urban crowdedness and longer commuting time that cancels off the positive effect of agglomeration economies.

We then calculate the gap between each city's actual size and its optimal size inferred from our estimation,as well as the efficiency losses in terms of GDP per capita due to its deviation from the optimal size.The results show that:although in general Chinese cities have been expanding in the past decade,there are still more than 80% of the cities in our sample having population sizes significantly smaller than their respective optimal levels in 2020; by contrast,some mega cities such as Beijing and Shanghai are significantly oversized and too densely populated.The efficiency losses caused by either undersize or oversize are remarkable; about 29% of our sample cities have a loss exceeding 50% of the potential GDP per capita they could generate under optimal size.

This paper also explores how to improve the city size distribution of China.We examine the correlation between the deviation of each city's actual size from its optimal size and the factor market integration in the province where the city is located.Specifically,we measure the degrees of integration of both the regional labor market and the regional capital market in 2010,which is ten years before 2020.We find that both increased migration easiness and enhanced capital market integration are negatively correlated with the gap between a city's actual population size and its optimal size (in absolute value).This may suggest that the integration of factor markets such as labor and capital markets can help achieve a more reasonable city size distribution in China.

Finally,as an attempt to evaluate cities' welfare instead of just GDP per capita,we construct a utility measure of representative worker for each city using both the city's GDP per capita and its air quality,following Freeman et al.(2019).We then investigate the relationship between the utility level thus calculated and city size.Again we find a significant inverted-U relationship.Moreover,from the point of view of utility level,while there are a large number of Chinese cities are undersized,a few megacities are oversized.The results are qualitatively similar to the previous main results based on GDP per capita.

Our paper makes the following four contributions to the literature.First,our study is conducted based on more accurately measured population living in urban core areas,which utilizes China's most recent population census data combined with POI and commuting data.We find that while a large number of Chinese cities are undersized,a few megacities are significantly oversized.The efficiency loss from either under-size or oversize is big.Second,we attempt to address an important policy question:How to improve the population size distribution of the Chinese urban system? Our study suggests that the integration of regional factor markets can help achieve a more reasonable city-size distribution for China. Thirdly,in addition to urban industrial structure,we find that the urban spatial structure is also very important to the fulfillment of the potential of agglomeration economies and hence influences the optimal city size.Finally,we construct a utility measure that incorporates both GDP per capita and the environmental quality and investigate the relationship between this more comprehensive welfare measure and city size.

At the end of 2020,China successfully completed its poverty alleviation efforts.However, a significant population of low-income individuals in rural areas,particularly in underdeveloped regions,remain vulnerable and may fall back into poverty.It is imperative to consolidate and expand upon the achievements of poverty alleviation during the transition period and effectively align them with the rural revitalization strategy.This can be accomplished through the establishment of a sustainable support mechanism to prevent the return of poverty.

This article commences by assessing the scale and challenges faced by the low-income rural population and analyzing the factors contributing to their low incomes.According to data from the National Bureau of Statistics,the ratio of per capita disposable income between urban and rural residents was 245 in 2022,reflecting the dual structure of  urban and rural areas in China.Furthermore,a significant number of rural residents have extremely low disposable incomes,with a lower proportion of property income.

Based on a comprehensive review and analysis of existing assistance measures and the associated issues,this article proposes recommendations for a sustainable support mechanism in the  post-transition period.

Firstly,it suggests implementing more proactive fiscal policies to increase transfer payments to rural residents,especially those in low-income groups,in order to narrow the income gap and stimulate consumption.This can be achieved by raising the minimum living standard,expanding the coverage of social assistance programs,and issuing long-term government bonds or special bonds to provide targeted cash subsidies or consumption vouchers.

Secondly,the article proposes the reform of the household registration system and the public service system,with a specific focus on promoting the urbanization of rural-to-urban migrant populations.The objective is to gradually narrow the 18-percentage-point gap in urbanization rates between permanent residents and registered residents.

Thirdly,the article highlights the importance of actively developing affordable rental housing to effectively facilitate the urbanization process of rural-to-urban migrant populations by enhancing the public service system.This can be achieved by providing capital from the housing provident fund center or the government,establishing a “development and construction guidance fund”, involving market institutions and financial institutions in financing,directly engaging in large-scale construction of rental housing,or acquiring existing assets for transformation into rental housing.Different supply models can be tailored to meet the diverse needs of various groups of new urban residents,enabling rural-to-urban migrants and new urban residents to settle in cities.Encouraging rental housing enterprises to develop and operate long-term rental housing in a more market-oriented manner can be accomplished.Once these projects mature,they can be exited through public REITs offerings to establish a closed-loop investment and financing cycle of “development → cultivation → exit → development”.

Fourthly,the article underscores the significance of promoting rural land transfer,improving land allocation efficiency,and achieving intensive and mechanized agricultural production to boost the total factor productivity of agriculture.

Fifthly,the article emphasizes the importance of investing in agricultural and rural modernization to raise overall factor productivity.It suggests expediting the establishment of a diverse input pattern,with fiscal support taking precedence,financial institutions providing crucial support,and active social participation.This will facilitate the comprehensive revitalization of rural areas and provide robust support for the development of modern agriculture,rural industries that benefit farmers,agricultural product processing and distribution industries,and emerging rural services.Additionally,it emphasizes the need to promote the construction of digital rural areas and smart agriculture,as well as the enhancement of rural living environment.

Lastly,there is a necessity to increase investment in rural human capital and establish an efficient vocational training system.This involves constructing a comprehensive,diverse,and hierarchical training system to promote the widespread improvement of the rural labor force's quality.By doing so,it can enhance labor market competitiveness and innovation capabilities,establishing a virtuous cycle between training for agricultural migrant workers,the accumulation of human capital,the upgrading of industrial structure,and the sustainable employment of the labor force.

With the maturation of artificial intelligence (AI) technologies,many companies have begun to innovate their business models using AI.This trend accelerated significantly following the successful release of ChatGPT 4.0,which has led numerous companies to integrate AI into their business strategies.Consequently,the innovation and evolution of business models have become focal points of discussion in both academia and the industry globally.

Despite the growing body of research on AI and business models in recent years,the findings on this topic are fragmented and there is a lack of a unified research framework for systematically understanding it.It is therefore challenging to identify the core themes in current research on AI and business models and key directions for future research.In response to this shortcoming in the literature,this study systematically reviews 70 key articles on AI and business models from leading international journals.By classifying and organizing this literature,the paper identifies four main research themes:① The impact of AI on business model innovation,including its influence on overall business models and their components;② Archetypes of business models based on AI;③ The evolution of business models enabled by AI;④ The co-evolution of AI capabilities and business models.Through summarising and analyzing current research,the paper proposes key areas for future research,including the impact of AI on business model innovation at firm and industry levels;the classification of AI-enabled business models into archetypes and the drivers and outcomes of each archetype;the interactive factors involved in,drivers of,and barriers to the evolution of AI-enabled business models;and the relationship between AI capabilities and business models from a co-evolutionary perspective.

This paper contributes to the field in three main ways.First,it synthesizes the themes and gaps in research on AI and business models,thereby advancing the study of business models in the age of AI.Second,it reveals that AI-enabled business models exhibit characteristics of complex adaptive systems,including self-iteration and adaptability.Finally,by systematically reviewing current research,it highlights future research directions,guiding subsequent studies on AI and business models.Overall,the paper provides new theoretical and practical insights into how AI is reshaping business models and outlines potential pathways for future research.

The paper is structured as follows.The first section discusses the theoretical and practical background of the study.The second section reviews and defines relevant concepts in AI and business models.The third section details the literature review process followed and analyses the 70 articles.The fourth section elaborates on the four research themes related to AI and business models identified in the study and discusses the gaps in the literature regarding these themes.The final section presents the conclusions of the study and the prospects for future research on this topic.

Pollution prevention and control is an important issue facing developing countries. Finding and evaluating effective environmental protection policies is the focus of environmental economics. This paper uses the difference-in-difference method to identify the overall impact and mechanism of the first round of the Central Environmental Protection Inspections(CEPI) in China. We find that the first round of CEPI led to significant improvements in AQI, PM2.5, PM10 and SO₂ in the inspected cities, but there were no significant changes in CO, NO₂ and O₃. CEPI functions through two mechanisms: bottom-up public attention and top-down environmental law enforcement.

Today’s Chinese women are expected to work and contribute to society,just like men,while simultaneously taking on most family responsibilities at home. The dominant belief is that only when women excel both at work and home,can they be deemed successful and content. This set of expectations not only represents social ideals for women but also serves as a personal life goal for many. Nonetheless,it is not clear whether work-family balance truly makes women happy and fulfilled. 
The happiness of women holds significant implications not only for the stability and harmony of individual families but also for the sustainable development of society as a whole. The current research  focuses on female time allocation and investigate how the distribution of time between work and family impacts female happiness. From an observer’s perspective,it seems that women who prioritize either family or work often find themselves at a disadvantage in both aspects:full-time homemakers feel less powerful at home,whereas work-oriented women may lack the support and warmth provided by their loved ones. Consequently,achieving work-family balance becomes an ideal state that promises women a heightened sense of well-being,perceived as a “double success”. Nevertheless,when viewed from the actor’s standpoint,balancing work-family time allocation exposes women to role conflicts and decision-making difficulties. For instance,work demands often disrupt family obligations and encroach upon precious family time. Moreover,women who strive for balance may perceive themselves as falling short in both domains when compared to those who prioritize their family or work and,as a result,feel unaccomplished. Hence,while the public tends to view balanced women as happier than family-oriented or work-oriented women,women who are actually balancing time allocation may not share the same sentiment and,paradoxically,view work-family balance as a “double stressor” in their life. In line with this paradox,psychological research has documented that people may “miswant” things that they do not actually enjoy because they are in different mindsets when predicting versus experiencing.
Data from more than 30 provinces and cities across China highlights the disparity between the predicted and the actual experience. To examine social perception,we randomly assigned 1,500 respondents to one of three distinct patterns of  time allocation:balanced (equal distribution of time between family and work),family-oriented (dedicating most time to family),and work-oriented (prioritizing the majority of time for work commitments). We find that people perceived balanced women to be happier than family-oriented (+13%) or work-oriented women (+22%). This social perception of “double success” holds across various demographics. 
In order to assess the actual well-being of women,we analyzed two nationally representative datasets:the 2010 China Family Panel Studies (CFPS) and the 2018 China Household Time Use Survey data. Despite the time lag and divergent measurements of time allocation,the findings are remarkably consistent:The more balanced the time allocation between work and family,the lower the reported levels of happiness among women. This pattern was most pronounced among women between 25 and 45 years old,when they were active in the workforce and shoulder most of the caregiving responsibilities at the same time. Thus,it seems that time invested in work and family constitutes a “double stressor” for balanced women. However,this pattern does not arise in men,which suggests that work-family balance primarily places a burden on women,compelling them to strive for excellence in both roles. Conversely,social expectations regarding male success predominantly focus on their professional achievements,with weaker expectations for them to take on family responsibilities.
To better understand the economic impact of time balance,we compared its effect size against the effect of income,which has a strong positive influence on female happiness. Our findings indicate that every hour toward the absolute balance (i.e.,work time=family time) decreases female happiness to the extent of losing a year of their income on average. Thus,the impact of balance on happiness is not only statistically significant but economically meaningful.
Our findings contribute to and expand upon previous research on the gap between predicted and experienced affective states. They also offer valuable insights into female happiness from the perspective of time allocation. Contrary to some findings in other countries,where the time balance between work and family increases time pressure but does not undermine female life satisfaction,the “double-stressor” phenomenon in China suggests that it may be unique to cultures where the public holds expectations for women to excel both at work and family yet social and institutional support for balancing is lacking. For policymakers and managers,our findings suggest that it is crucial to assist both women themselves and society at large in breaking free from the illusion of “double success” imposed on the “ideal woman”. By challenging the entrenched beliefs and perceptions surrounding the notion of “having it all,” women can attain more freedom in choosing and managing their own time and happiness. In addition,companies should (continue to) promote flexible working hours for female employees. Related policies such as remote work options and on-site childcare facilities can help improve the happiness,productivity,and self-efficacy of women in the workforce. 

This paper reviews the literature on spatial panel data models in econometrics.In recent decades,panel data models with spatial interactions have become increasingly important in empirical research,as they account for dynamic and spatial dependencies and control for unobservable heterogeneity.With panel data,we can not only have a larger sample size to improve the efficiency of the estimators,but also investigate some problems that cross-sectional data cannot handle,such as heterogeneity and state dependence across time.

This paper first introduces various spatial panel data model specifications,which are divided into two categories:static spatial panels and dynamic spatial panels.For static spatial panel data models,the regressors do not include a time-lagged term,but the disturbances can have serial correlation along with the spatial correlation.Depending on whether the individual effects are correlated with regressors,we have fixed effects models and random effects models.For spatial dynamic panel data models,we need to consider the influence of the initial period,and the length of time periods is important for asymptotic analysis.Depending on the eigenvalue structure of the dynamic process,we have stable,spatial cointegration,unit root,and explosive processes.Besides these two benchmark models,various model specifications are proposed in the literature,such as the semiparametric approach,common factors,endogenous spatial weights matrix,simultaneous equations model,and structural change.

We then introduce corresponding estimation methods in detail for the two benchmarks,including quasi-maximum likelihood estimation and generalized moment method.For the static spatial panel data models,we present likelihood approaches for the fixed and random effects models.For the fixed effects model,we can either estimate those fixed effects directly along with the regression coefficients,or transform the data to eliminate those fixed effects and then perform the estimation.The latter transformation approach can avoid the incidental parameter problem and yield consistent estimation for all parameters.For the random effects model,we do not have the incidental parameter problem and the estimators are more efficient than those from the fixed effects model.In the literature for spatial panel data,the Hausman test is proposed for the model specification,which is also applicable to a general static spatial panel model with serially and spatially correlated disturbances.For the dynamic spatial panel data models,we first present likelihood estimation for various spatial dynamic panel data depending on their eigenvalues.Even though the maximum likelihood estimator is consistent over a long time period,asymptotic bias will still invalidate the statistical inference.Thus,a bias correction procedure is recommended to eliminate the asymptotic bias,where the bias formula might take different forms depending on the stability feature of the data-generating process.We then review the GMM estimation utilizing both linear and quadratic moments.Compared with QML estimation,the GMM is computationally convenient,is valid regardless of short and long time periods,and is applicable when the spatial weight matrix is not row-normalized under the model with time effects.The GMM estimation can be as efficient as QML estimation,and more efficient if the disturbances are not normally distributed.For both QML and GMM estimation,estimation and inference might be invalid under cross-sectional heteroskedasticity.We review recent work on this issue,including adjusted QML estimation and recentered method of the moment.The adjusted QML makes the bias correction for the score vector of the likelihood function,while the recentered method of moments investigates the correlation of endogenous regressors and disturbances.

Finally,the semi-parametric estimation of spatial panel data models in recent years is reviewed,where spatial weights matrix,exogenous regressor,spatial lag,or regression coefficients could be nonparametrically specified.In conclusion,we expect that dyadic data and nonparametric model specification tests for the spatial panel data can be two promising fields in future research.

In the past seventy years since the sale of the first mainframe computer by IBM in 1953, the world have become an information society that relies on networked computing systems in all aspects of work and life. Along with the evolution of computing, Information Systems have become an indispensable discipline in business and management that serves the needs of other managerial disciplines and performs its  business functions. Further information technology (IT) has become a sine qua non for the survival of a business and a source of much competitive advantage. Without advanced IT systems, an organization cannot survive for long today. In this paper, we recount and evaluate the evolution of Information Systems research since its infancy in the 1980s by empirically analyzing the research that has been published in leading academic journals. We found that research in Information Systems has evolved through several stages. During this evolution the basic disciplines from which researchers have drawn have also shifted considerably. Over this period the underlying technology support for the research has also developed enormously from simple data processing to systems development, client-server networks, electronic commerce, several generations of the Web (1.0, 2.0 and 3.0) and blockchain. Each new technology generation has spawned its own unique set of research issues and in turn influenced the research agenda of the scholars in the IS field in terms of research topics, methods, and theories. History offers a mirror to the past and a lesson for the present, and thus we believe that the results of this study should provide instructional value for new researchers such as doctoral students in Information Systems and other related disciplines.

This paper reviews the monetary policy evolution of advanced economies during 2008-2022, which  is a special period with radical changes, and  covers two global crisis: the 2008 Global Finance Crisis and the 2020 Covid-19 Pandemic. This paper provides a comprehensive analysis regarding three dimensions of monetary policy evolution of advanced economies: monetary policy implementation, monetary policy mandates, and policy tools and transmission channels, and particularly unconventional monetary policy like “Quantitative Easing”, conducted by central banks of advanced economies since the 2008 Global Finance Crisis. In the end, The paper also provides inspirations and suggestions to China, based on the multiple challenges and downturn pressures the Chinese economy is facing right now. 

This paper shows, theoretically and empirically, that the organizations of the Communist Party of China (CPC) play an important role in improving corporate social responsibilities (CSR). In the baseline model, the objective of enterprises is to maximize firm value, while the objective of the CPC is to maximize social welfare. Consequently, the free market system without the CPC's guidance leads to inefficient CSR commitment, and the leadership of the CPC could help to improve CSR by incorporating social welfare into the firm's objective function. The baseline model also reveals that the positive effect of the leadership of the CPC on CSR is more pronounced when the firm itself cares less about social welfare initiatively. Then in the extension model, considering the information asymmetry between the Central Committee of Party (CCP) and the enterprise-level committees of the party (ECP), we show that in an economy with the CCP only or the ECP only, the CSR cannot reach the social optimum either. Only when the CCP provides information on overall CSR demand, the ECP provide enterprise-specific guidance according to the CCP's information, and enterprises manage production activities under the ECP's guidance, can optimal CSR be achieved. The empirical results among A-share public firms provide support to the theoretical predictions. Overall, our paper highlights the important role played by the CPC in corporate governance.

This paper presents a new theory on the US exorbitant privilege that it is service fee paid by the rest of the world to the US in contrast to the conventional view of insurance premium. In good times, the rest of the world buys low yield US treasuries for convenience purposes, while the US buys high yield foreign assets. In crisis times, the implicit fee becomes larger as the global financial institutions demand liquidity offered by the US treasuries. I build a two-country DSGE model to illustrate my service fee mechanism.

The persistent disconnection between management theories and practices has been a longstanding concern in organizational research.This disconnection becomes particularly problematic in the era of Artificial Intelligence (AI),where technological advancements are fundamentally reshaping organizational elements and logic.Conducting phenomenon-based research on human-AI collaboration has emerged as a crucial pathway for advancing theoretical development and real-world impact.

This paper begins by reviewing the current state of human-AI research,identifying three emerging themes central to understanding human-AI dynamics:human reaction to AI,human-AI interaction,and human-AI collaboration.The “human reaction to AI” theme predominantly examines individual responses to AI (and AI-generated information) during episodic interactions,characterized by vignette-based scenarios where AI does not directly impact work-related outcomes.This body of literature can be categorized into two main streams:research on AI/algorithmic decision accuracy,focusing on human assessment of computational reliability and precision; and research on AI decision legitimacy,focusing on human acceptance of moral judgment.While these studies provide valuable insights into human trust formation and AI perception,they often lack ecological validity.The “human-AI interaction” theme investigates how AI directly influences work-related outcomes,also during episodic interactions.This body of research often adopts controlled laboratory settings,exemplified by studies on ChatGPT’s impact on professional writing output and human reasoning capabilities.These studies highlight the growing relevance of AI’s immediate impact on both individual and organizational performance,emphasizing the necessity of understanding how AI interacts with human work in real-time.The “human-AI collaboration” theme addresses the evolving nature of human-AI coexistence in workplaces.AI has increasingly become embedded in work processes,creating complex,real-world challenges about how humans and AI collaborate over time.This stream of research examines dynamic,longitudinal interactions where humans and AI systems reciprocally influence outcomes across multiple dimensions,including work design,learning strategies,and long-term performance.These studies,grounded in specific organizational contexts,identify theoretical mechanisms and intervention strategies through fieldwork.

To demonstrate how phenomenon-driven research advances theoretical development,this paper analyzes an exemplary ethnographic study from the Academy of Management Journal.This study reveals how researchers can construct robust theory by systematically investigating the human-machine interface as it unfolds within authentic organizational settings.It also illuminates how richly contextualized insights from immersive fieldwork can effectively bridge theoretical development with practical implications.

After reviewing existing studies on human and AI,we further revisit seminal theories in human-computer interaction literature.These classical frameworks advocate for a system-level perspective that considers technological integration within broader institutional structures and organizational dynamics.By tracing the evolution and enduring relevance of these foundational theories,we call for a theoretical approach that recognizes the deeply embedded and interconnected nature of human-AI collaboration within complex organizational ecosystems.The goal is to inspire contemporary researchers to embrace more holistic,context-sensitive approaches when investigating emerging human-AI phenomena.

Informed by the latest studies as well as grounded in cutting-edge integration of technology and organizational settings,we here propose several promising research avenues that shed light on both theoretical advancements and managerial applications.First,we encourage exploring how employee experience and collaboration with AI jointly interact,focusing on how different experience levels impact collaboration outcomes.We should further unravel how to enable employees to maximize AI’s potential as well as alleviate the negative side.Second,we suggest that future researches may investigate how AI feedback,trust,and job security concerns affect performance in complex tasks,and how adaptation to and dependency on AI may impact employees’ skill development in the longer term.Third,we encourage researchers to examine how distinct collaboration modes between AI and humans in different contexts could optimize performance.Last,researchers can delve into users’ responses to AI suggestions and explore mechanisms to balance trust in AI and independent judgments in terms of decision-making.

The era of AI requires management scholars to bridge the gap between theory and practices by updating,transforming and transcending existing research paradigms.By uncovering deeper insights into the intricate dynamics of human-AI interaction,we shall contribute to richer and more comprehensive theoretical layers and potentially great theoretical breakthroughs.

Bitcoin,created in 2009, symbolized a brand new stage of digital currency (“DC”).In general,digital currency can be classified as private DC and CBDC(central bank DC):the former has the two main categories of Bitcoin-like cryptocurrency and USDT-like stablecoins while the latter has a retail type and a wholesale type.Overall,the practice of digital currency is still in its early stages,and academic research has not been going on for long.The mechanisms and rules of different digital currencies vary greatly,and scholars' perspectives and methods differ,with disagreements and controversies on many issues.Therefore,it is necessary to systematically review and comment on the literature.Moreover,literature on digital currencies has grown rapidly in recent years,particularly those related to stablecoins and CBDC.This article attempts to review the representative literature of DC and important policy discussions regarding CBDC.The ultimate goal is to point out future research directions of CBDC,particularly for China's CBDC (e-CNY),and to stimulate further thoughts on China's digital currency policy practice.

Such work should be highly meaningful,as China's central bank's policies on digital currency are quite advanced globally,such as banning private digital currency transactions and developing CBDC early on,but the domestic academic community has not provided so much serious and in-depth academic research in this area,and there is not enough support for policy practice.As digital currency development is surging globally,China's central bank is at the forefront,and the academic community should catch up.

In the main body,this paper aims to comprehensively review the international frontier literature and policy reports on digital currency since 2009,based on the two main threads:private DC and CBDC.Specifically,in the field of private DC,it focuses on international literature related to private digital currency and blockchain technology,covering four themes:the operation mechanism of cryptocurrency,competition among various digital currencies,cryptocurrency transactions and industrial organization,and the rise of stablecoins and their impact.In the field of CBDC,it mainly reviews international literature on CBDC,covering five major themes:whether to issue CBDC,its impacts on financial intermediaries,firm investment and welfare,CBDC and privacy,CBDC and financial stability,and policy discussions on CBDC.Furthermore,given the intensive pilots of China's CBDC since 2019,this paper particularly uses one section to discuss policy issues of e-CNY,e.g.,if e-CNY should stick to the current status/definition as being M0 and retail CBDC.In the end,it points out the future research direction of CBDC,particularly of e-CNY,including optimal design from consumers' perspective,analysis regarding e-CNY used for cross-border transactions,impacts of the two-tier system on the payment industry and data market,impacts on monetary policy and financial stability,and other dimensions of future pilots and explorations,etc.

Using daily news tone data from 2017 to 2020, we examine whether news tone can predict stock returns in the Chinese A-share market. We find that news tone significantly and positively predicts the cross-sectional stock returns over the next day and the next 12 weeks. When the news is separated into online news and paper news, the former exhibits strong predictive power for future returns, while the latter only has marginal predictive power. For this difference, we hypothesize that the online news likely reflects information about firm fundamentals, while the paper news mostly focuses on SOE firms. Our results using earnings surprises and SOE subsample provide supportive evidence for the hypothesis. 

Micro-and-small enterprises (MSEs) constitute the capillaries of China's socio-economy,and play a pivotal role in fostering high-quality development of the Chinese economy.Despite their significance,a nuanced understanding of MSEs has been hindered by the absence of detailed micro-level data.Addressing this gap,Peking University's Center for Enterprise Research and the Institute of Social Science Survey,in collaboration with Ant Group Research Institute,has been conducting regular surveys on MSEs since the third quarter of 2020.These quarterly surveys comprehensively address various aspects,including the basic business situation of MSEs,challenges faced,policy benefits received,financing and digitalization.This paper presents a comprehensive introduction to the design,implementation and features of the Online Survey for Micro-and-Small Enterprises (OSOME) in China.It delves into the prevailing business landscape of China's micro-and-small merchants by leveraging data collected through OSOME.In addition,drawing on data accumulated over the past 13 consecutive quarters,this paper constructs the “China Micro and Small Business Confidence Index”.This index captures four essential dimensions:market demand,business revenue,operating costs,and the anticipated number of employees.By doing so,it offers a novel perspective on the expectations of micro and small merchants.

The study delineates five distinctive characteristics of China's micro and small operators.Firstly,there exist a substantial number of unregistered self-employed merchants in China,a segment frequently overlooked in prior business surveys and statistics.This group,however,is critical for a comprehensive understanding of China's market dynamics.Secondly,the primary existential challenges confronting micro and small merchants in recent years revolve around weakened market demand and escalating operating costs.Thirdly,in contrast to their medium and large enterprise counterparts,small and micro operators exhibit a heightened reliance on online financing channels,with online financing prevalence surpassing that of offline alternatives.Fourthly,the proportion of policy benefits,such as tax and fee reductions,enjoyed by micro and small operators is relatively modest.Lastly,micro and small businesses are intensifying their digital transformation efforts to navigate the evolving market and economic landscape.

The implementation of the OSOME survey holds significant importance in facilitating a nuanced comprehension and targeted support for micro and small operators in China.OSOME extends its reach to a substantial number of micro and small operators,particularly those operating on a small scale or without formal registration.This inclusive approach offers a direct channel for these operators to voice their concerns,thereby allowing for a more accurate understanding of their actual needs and business challenges.Moreover,the regular quarterly data gathered by the surveys serves as a valuable tool in capturing the dynamic evolution of the business landscape and information in other dimensions related to micro and small operators.This,in turn,provides crucial data support for both academic research and policy formulation.Lastly,the implementation of OSOME also paves the way for a novel avenue in social science research methodology,showcasing the key role and potential of big data technology in advancing research in social and economic domains.

This paper represents a pioneering effort in China,being among the first to systematically conduct a survey and undertake an in-depth analysis of the country's micro and small operators.It effectively addresses data gaps in the study of MSEs,offering robust support for related academic and policy research.

The construction of a valuation system with Chinese characteristics is a crucial step towards enhancing the adaptability and pivotal role of the capital market and supporting the high-quality development of China's economy.This paper systematically addresses three key questions regarding the construction of the valuation system with Chinese characteristics.Firstly,which pricing factor is important for the Chinese valuation system? Secondly,has this factor been incorporated into the current market valuation models? Thirdly,if not,how can it be included in the valuation system?

We propose that the valuation system with Chinese characteristics should consider the total social value created by firms,rather than solely focusing on the net present value of profits for shareholders.The measure of a firm's total social value should encompass two aspects:①stakeholder value,including the benefits to employees,suppliers,customers,debt holders,and governments; ②the multiplier effect that a firm generates for the overall economy through its production network.This measure not only reflects an orientation towards improving people's welfare but also fully embodies the prominent features of the importance of the node industry in China's economic growth model.

We empirically examine whether the total social value has been priced using China's A-share sample stocks from 2003 to 2021.The results indicate that firms with high social value demonstrate superior fundamental performance.The portfolio of high social value stocks can generate excess stock returns that cannot be explained by existing pricing factors.Investors are more likely to underestimate the earning performance of firms with strong social value.These findings suggest a mispricing and undervaluation of the social value factor in China's current capital market valuation system.

The reconstruction of the valuation system with Chinese characteristics is an ongoing and lengthy process.It requires simultaneous efforts from the investment side and the corporate side to mutually cultivate capital and assets that recognize social value.Our policy recommendations include:Firstly,standardizing and strengthening the disclosure of information related to corporate social value.Secondly,enhancing investor education and leveraging the role of professional institutional investors to focus more on information related to social value.Thirdly,from the investment side,cultivating long-term capital for social value,such as insurance,pensions,social security,and annuities,and developing broad-based index products.Fourthly,encouraging Chinese enterprises to proactively strengthen their strategic and operational management capabilities in creating social value.Lastly,strengthening systematic research on the valuation system of Chinese characteristics.

Since the reform and opening-up,China has experienced rapid economic growth and become the largest economic entity in Asia and the second in the world. Gradually,it has grown into an important engine driving global economic growth. China's macroeconomy has also received more attention from the academic community and become a key research object. Studying China's macroeconomy plays a crucial role in optimizing the government's macro-control policies,addressing structural issues in the economic system,and maintaining healthy development of China's market economy. Today's world is in the midst of great changes that have not been seen in a century. Although peace and development remain the theme of the times,the risks and uncertainties of world economic operations have significantly increased. Against the backdrop of a downturn in the world economy,volatile international situation,and domestic reform pains,China's macroeconomic data are often subject to structural changes,wherein different economic sectors present strong interlinkages and co-movements. Understanding the phenomenon of structural breaks in China's macroeconomy plays an indispensable role in enabling the government to better perceive the risks and uncertainties of the world economy's operation,grasp China's macroeconomic structure and laws of development,and evaluate the effectiveness of regulatory policies. Thus,the structural change analysis of China's macroeconomy has become an urgent need for accurately predicting economic development trends,improving the macroeconomic control system,and promoting the overall macroeconomic stability and high-quality development.

In recent years,the analysis of the structural breaks in China's macroeconomic data has received increasing attention. However,there are two shortcomings in the existing literatures,which make it difficult to capture structural changes in the macroeconomy. First,the current studies mainly use the sequential testing procedure to detect the existence and locations of the structural changes,but the sequential estimation procedure would involve the multiple testing problems. As a result,it is more likely for the test to reject the null of no structural changes,and then the number of breaks will be overestimated,thereby affecting the subsequent analysis of structural changes. Therefore,to avoid the impact of sequential testing procedure on the structural breaks detection,some researchers reformulate the identification of multiple structural breaks as a variable selection problem in the high-dimensional regression with group-sparse coefficients. Then,they can apply the shrinkage estimation methods to estimate the numbers and locations of structural changes simultaneously. This type of method has the advantages of fast calculation and high accuracy for identifying structural changes. Second,the existing literatures generally ignore the inner relationships among various macroeconomic variables,only analyzing the structural changes in a certain field of national economy with a single macroeconomic variable. However,there exist close internal connections among these macroeconomic variables actually. Therefore,depicting and utilizing their coordinated changes is crucial for the government to analyze China's overall macroeconomic operating situation and regulate the macroeconomic system. In recent years,more and more researchers have tried to construct potential factors that can capture macroeconomic dynamics to analyze China's macroeconomy. However,they usually assume that the factor loadings do not change over time,ignoring the impact of technological innovation,policy reform and other uncertain factors on macroeconomic data. Consequently,the constructed factor models may not accurately capture the common factors behind the China's macroeconomic data. To capture structural changes in factor models,Baltagi et al. (2021) and Ma and Tu (2023a) recently proposed estimation methods based on the least squares and Group Fused Lasso,respectively. The theoretical contributions of Ma and Tu (2023a) include the consistency and limiting distribution of the break fraction estimators and consistent break date estimators,rather than just the consistency of the break fraction estimators (Baltagi et al.,2021). In practice,the procedure of Ma and Tu (2023a) is practically easy-to-implement with standard statistical packages,overcoming the drawbacks of the existing methods that they often involve multiple tuning parameters and are computationally demanding in dealing with multiple unknown breaks. Therefore,the method of Ma and Tu (2023a) is both theoretically accurate and practically appealing,and has the potential for analyzing structural breaks in China's macroeconomy.

In summary,China's macroeconomic data typically exhibit characteristics of structural breaks,and there exists a factor model structure,where macroeconomic variables in different sectors have strong co-movement features. However,few scholars have applied factor models with multiple structural breaks,together with an efficient and accurate estimation method,to study the structural break phenomenon in China's macroeconomy. This paper aims to fully utilize the factor characteristics in China's macroeconomic data and accurately capture the structural breaks in the macrosystem,providing a comprehensive analysis of China's macroeconomy. In particular,this paper proposes the factor model with multiple unknown structural breaks to model 23 China's macroeconomic time series,and further utilizes the estimation method of Ma and Tu (2023a) to identify the unknown numbers and locations of the change points,in contrast to Baltagi et al. (2021). The results show that via the estimation method of Ma and Tu (2023a),China's macroeconomic data have undergone six structural breaks from 1990 to 2022,and the estimated break dates are closely related to the important historic events,such as,Deng Xiaoping's South Tour Speeches and the 14th National Congress of the Communist Party of China,the SARS  in 2003,the  COVID-19  in 2020,etc. The method of Baltagi et al. (2021) detects four structural breaks,however it fails to detect the structural breaks in 2008 and 2020. This shows that the method of Ma and Tu (2023a) benefits from its more accurate change point estimation and fewer tuning parameter choices in practice,so that it can identify more structural breaks in the macroeconomic system and better capture the dynamic development of the macroeconomy. In addition,thanks to our factor model considering synergistic relationships in macroeconomic data,as well as a more accurate method for estimating change points,this paper can simultaneously detect the structural breaks in China's macroeconomy which were only separately discovered for each series in the existing literatures. 

Bridge loan financing,as a short-term and provisional informal financing tool,can help firms rollover loans at the micro-level and transfer risks across time at the macro-level. Though bridge loan borrowing is quite pervasive in practice,we still lack a general picture of what is going on. To this end,we match  loan-level data from 19 large-scale commercial banks and financial data of firms listed on the Chinese A-share market from 2007 to 2013. With these in hand,we are able to conduct some preliminary empirics to offer several stylized facts of Chinese A-listed firms involved with bridge loan borrowing. The following results are obtained by comparing firms with bridge loan and those without bridge loans. First,those firms with bridge loan are associated with lower profitability and higher leverage ratio in the past,implying that those firms must once undergo negative business shocks and have to bear higher level of debt to be repaid before they borrow bridge loans. Second,those firms with bridge loans have higher financial costs,mainly because financing via bridge loan is more costly. Thirdly,the future return on equity and Tobin's Q for firms with bridge loan turn out to be lower. This might be because the exceptionally high financial cost of raising funds from bridge loan hinders those firms from making more productive investments and therefore worsens their long-term performance. The above empirical evidence is the first step for understanding the cause and consequence for firms to borrow bridge loans. 

Based on the stylized facts,the paper then constructs a two-period game-theoretical model with bank,loan officer and firm to examine the mechanism on how bridge loan financing affects financial stability. There are a continuum of firms in the economy. We first assume that the productivity of each firm is independently drawn from a distribution function across two periods and call this the “transitory productivity shock” case. Later we discuss the “persistent productivity shock” case where each firm's productivity is the same across two periods. Each firm has to borrow a unit of fund from bank to finance their investment in period one and two. Those firms with poor realized earnings in the first period are unable to pay their debt back in the end of the period,so they would like to borrow bridge loan to repay their debt first and then get funds for period-two investment. Those firms that fail to repay their first-period debt go bankrupt and the bank only recoup a part of their salvage value from those failing firms. However,the bank cannot observe the realized output of a firm while the loan officer can know this key information. The objective function of a loan officer has two components:one is that the bank wants him to release more loan,the other is that the bank also aims to constrain default risk from bad firms by detecting whether the loan officer continues to grant a loan to a firms involved with bridge loan and if so,the loan officer would therefore be punished. In equilibrium,the loan officer continues to grant a loan to firms whose productivity is above an endogenous cutoff and rejects to grant a loan to firms with productivity below such cutoff. The firms that survived from the first period are composed of two parts:those “healthy firms” that do not borrow bridge loans and those “sub-healthy firms” that have to borrow bridge loans. A “healthy firm” would invest the full part of loan. As  contrast,a “sub-healthy firm” needs first pay back to the bridge loan company at a high interest rate and then invest the remaining proceeds,but the bank still charge the repayment by a full loan scale,so the default risk of those “sub-healthy firms” becomes high. This is  the cost induced by bridge loan from the perspective of bank. The associated benefit,directly speaking,is that those “unhealthy firms” avoid going bankrupt in the short run and have a chance to survive to the next period. 

Our model has drawn several important conclusions. First,bridge loan is able to stabilize a firm's financing and maintain its investment and production in the short-run,especially for those firms affected by liquidity shocks,and consequently delays financial risks to the future. Second,when confronting the tradeoff between inter-temporal risk transfer and two-period profits,the regulatory authority may have different attitude towards bridge loan from commercial banks,as the former's objective is to maximize the financial stability for the whole system while the latter's is to maximize expected profit without taking systemic risk induced by bridge loan borrowing into consideration. The optimal size of bridge loan lending chosen by commercial banks is suboptimal in terms of financial stability,so the regulatory authority must step in to oversee commercial banking and constrain their negative externality on financial system. Third,as the likelihood of systematic crisis goes up,the regulatory authority should adopt heavier regulations on bridge loan financing. That is,the regulatory authority must take a more stringent measure to control the bridge loan borrowing.

What's more,our model also sheds light on the contemporary debate on whether the loan officer should be accountable for granting loans for lifelong. We characterize the loan officer's incentive under the lifelong accountable system and compare it with the benchmark case of “bridge-loan-detecting system“ where the loan officer would be only penalized if he has been detected to grant a loan to a firm with bridge loan borrowing. We find that the optimal financial stability under the two systems can be the same,but contracting under the lifelong accountable system is less robust. More precisely,when the environment changes and the policy cannot be adjusted in time,the financial risk under the lifelong accountable system turns out to be higher. Our analysis reveals that the so-called “lifelong accountable system” does not looks perfect as it sounds like. Finally,we also discuss the economy where there are two types of firms in the economy:firms subject to transient liquidity shocks and firms subject to permanent production shocks. The production shocks experienced by the former in the two dates are independent with each other while those experienced by the latter are the same. If the bank can tell one type from the other,it would rather lend to transiently-shocked firms rather than permanently-shocked firms. However,firm's type is private information and only known to itself. As the proportion of firms affected by permanent shock increases,the negative impact induced by bridge loan borrowing on financial stability in the long run grows. As a result,the optimal scale of bridge loan lending should be consequently downsized,until all such lending activities should be banned.

In today's digital business environment, consumers have become savvy smartphone shoppers, moving swiftly among apps, mini-programs, social media, shopping platforms, and company websites as they acquire information, seek views from friends and relatives, follow recommendation posts of KOLs, search for better deals, and eventually shop.As such, every business needs to change and become as sophisticated as their customers, not only to continue to provide suitable products and services, but to utilize new technologies to ensure smooth customer experiences and track their shopping journey in real-time.Among all consumer behaviors, customer defection is the most critical issue they care about because customer churn, if not prevented in time, means the loss of future business.To actively prevent customer defection, businesses need an early detection system.Unfortunately, unlike big companies, it  is difficult for small and medium enterprises (SMEs) to do so because most of them are short in resources, people, and know-how to operate a sophisticated CRM system.

In this paper, we develop a Brownian motion model based on the level, drift and volatility of usage rates of individual customers.An important feature of our model is its simplicity of implementation and interpretation: it can be calibrated with very few observations, at the individual customer level, and using standard business technology (e.g., spreadsheets such as Excel).We build and empirically demonstrate that this approach can simultaneously provide real-time predictions on both future usage rates and churn with performance comparable to several more complex benchmark models.Our approach is simple unlike the “black box” offered by SaaS companies.It leverages key parameters derived from customer's product usage rate and, as we show, these parameters enable useful customer diagnostics linked to future usage and cashflows, and the risks of customer defection.We find that volatility is positively related to and is a better leading indicator of churn than drift.The approach lends itself to simple management and evaluation of customers of SMEs.

Specifically, we contribute to the literature on CRM at a general level and churn management by proposing a simple, easy-to-implement model that uses each customer's historical usage pattern as a leading indicator of churn.The model we develop can greatly aid managers in proactively targeting individual customers at risk of churning.Using a de-classified telecom dataset, we demonstrate that the simple GBM model performs better than several benchmark models and about the same as the more complicated dynamic linear model in predicting customer churn.The model has several managerially desirable features related to implementation, diagnostics, and predictive ability that we discuss below.

Overall, based on several relative and absolute performance metrics, we find that the model performs very well in both short-range and long-range forecasting of whether a customer will defect, and on forecasting usage rates.The model also yields two readily interpretable diagnostics—drift and volatility—that can help monitor customer “health” in managerial dashboards.Customers with negative drift and high volatility, for example, are potentially good targets for intervention.Aside from helping to identify which customers are at risk of defecting, these customer-level risk-return diagnostics can help continuous service providers manage usage-related cash flow risk for active.Firms can segment customers with similar risk-return, i.e., usage rate's drift and volatility, profiles and target them with customized services (e.g., calling plans, add-on phone features).We estimated the impact of customer characteristics, VIP status, changes in features and use of add-on services on these diagnostic parameters.These estimated effects could help managers, for example, in improved assignment of VIP levels to customers to manage usage rate drift and volatility.

We compared the predictive performance of our GBM-based model with several benchmark models commonly used in the marketing literature for churn prediction and found that the individual level GBM model outperformed other models on all but two of the measures considered.We also estimated a dynamic linear model capable of capturing more flexibly the dynamics in usage rates; we found that our GBM-based models performed about the same as this model.Since the predictive performance of the two GBM-based models is equivalent, we favour the “Individual Level” model because it is easier to implement.In cases where managers want to learn about the drivers of usage rate's drift and volatility with the goal of guiding policy toward managing them or making predictions about new customers for whom no usage rate data exist, the “Multilevel” model is appropriate.

We view simplicity, both in estimation and interpretation, as an important strength of the modelling framework, because simple models are more likely to be used by managers.Our model is suitable for most contractual settings, and some non-contractual type settings (such as casual gym memberships), if a customer can be identified across instances and where there is some ongoing usage that can be observed or inferred over time.It would obviously not be useful in contexts where only cross-sectional data are observed or where customers utilize a physical good, service, or experience only sporadically and very infrequently (e.g., wedding dress, birthday party venue).We speculate that our model will become much more relevant to durable consumption, or even health tracking, as increasingly these situations are being digitally transformed and companies can make use of first-party data from such applications.

This paper study the heterogeneous returns on investment and the price dispersion in the real estate market, using the pre-owned house transaction data in Beijing from 2008 to 2017. Results show that the returns to real estate investment have significant heterogeneity, and reveal that China’s real estate market has price dispersion. We also find that the distribution of house prices has a fat tail, and calculate the Pareto index. These results have important policy implications in the background of deleveraging and possible implementation of real estate taxes.

Government policies are often difficult to measure. This is especially true in China, where local governments have numerous, formal or informal, policy tools at their disposal.  This paper propose a measure of pro-innovation policy effort by counting the number of articles mentioning “专利” (patent) in each official provincial newspaper and deflating it with a proxy for the number of total articles. We then examine the effect of such policy measures on the patenting activities of listed firms from 2001 to 2010. To deal with policy endogeneity, we adopt an instrumental variable approach that leverages on the possibility that provincial-level disaster relief activities compete for governmental attention and resources devoted to innovation. Our results show that innovation policies increase the number of patent applications filed by listed firms without reducing their quality. This effect is most salient on the extensive margin. Non-state-owned enterprises (SOEs) are more responsive to innovation policies, partly because they are more likely to be on the extensive margin.

Using GDP as a macro-policy objective tends to overstate a nation's macro leverage ratio and understate its potential to employ countercyclical policies during economic downturns. In this paper, we propose using the complete value (CV) instead of GDP as the objective for macro-policies and construct a policy space variable accordingly. Including the policy space variable in the financial crisis prediction model in Greenwood, Hanson, Shleifer and Sørensen (2022), credit growth and asset price boom lose their predictive power of financial crisis. Moreover, we find that the effect of policy space to prevent financial crisis is more pronounced in economies with higher TFP growth and investment efficiency. Our empirical findings suggest that the Kindleberger-Minsky model of financial crisis is less applicable to countries with sound fundamentals and ample policy space. This paper hence argues that China has sufficient policy space for proactive macro-policies with its CV being above the headline GDP and its long-term steady growth rate being above the public debt interest rate. We propose a macro-policy framework based on the CV and recommend several policy measures to help move the Chinese economy back to a steady growth trajectory.

The text incorporates long-term defaultable corporate bonds and credit risk in a dynamic stochastic general equilibrium business cycle model. Credit risk amplifies aggregate technology shocks. The debt-capital ratio is a new state variable and its endogenous movements provide a propagation mechanism. The model can match the persistence and volatility of output growth as well as the mean equity premium and the mean risk-free rate as in the data. The model implied credit spreads are countercyclical and forecast future economic activities because they affect firm investment through Tobin's Q. They also forecast future stock returns through changes in the market price of risk. Finally, The model shows that financial shocks to the credit markets are transmitted to the real economy through Tobin's Q.

In a dual economy with a continuum of endogenous wages, we provide both optimal income tax and commodity tax formulas with sufficient statistics. We treat the sector with higher income and lower substitution elasticity between labor factors as urban areas, while the other one as rural areas. We find that both commodity and income tax depend on wage elasticities with respect to labor inputs. Higher commodity tax rate should be levied on the goods produced by high-ability individual relative intensive sector, which in turn makes income tax flatter. Considering endogenous wages, labor mobility and commodity taxes, we generalize the standard optimal income tax formula. The numerical simulation is based on a survey data on urban and rural household income in China in 2007 and 2013. We find that urban commodity tax is always higher than rural commodity tax, while the difference is relatively smaller in 2013. Besides, given the substitution elasticity between labor factors in urban areas, the difference between the commodity tax rates of urban and rural areas are increasing with the decrease of substitution elasticity in rural area. 

Unlike much of the existing literature, which focuses primarily on the potential for automation technology to replace labor, we believe that progress in automation technology and the labor market are equally important.  By taking advantage of the 2004 enlargement of the European Union and the subsequent influx of cheap labor from new member states into the UK, we find that occupations or industries that were more impacted by this influx are less likely to adopt automation equipment.  Further analysis indicates that discrimination keeps the wages of immigrant labor below equilibrium, incentivizing firms to substitute automation with less expensive immigrant labor.  To eliminate potential endogenous problems, we also use the same shock that Ireland experienced as an instrumental variable.  Even when using robot installations as an alternative metric for the application of automation technology, the results remain robust.  Our mechanism test reveals a larger substitution effect in occupations where the wage gap between immigrant and local labor is greater.  Our findings highlight the importance of labor price in determining the adoption of automation technology, providing a valuable supplement to existing research on the relationship between automation technology and labor.  This study also has important policy implications, indicating that in the promotion of intelligent manufacturing with automation as the core, policies relating to labor will, in turn, affect the adoption of automation technology. 

As data visualization has become increasingly common, consumers’ decisions are more likely to be influenced by a visualized reality. In this paper, we demonstrate and explore how the effects of price history attributes on consumer purchase are sensitive to visual display through a series of experiments. The experimental results suggest that the effect of  the direction of the last price change is sensitive to both visual magnitude of price change (VMPC) and visual length of time horizon (VLTH), and the effect of price change times is more sensitive to VMPC than to VLTH. Besides, when VMPC is small and VLTH is long so that price changes are less visually salient and less visually relevant to the current price, we find that a longer time horizon under such visual display has significantly positive effect on consumers’ purchase likelihood. 

Data has become the core element of the digital economy while opaque data policies introduced by platform raise concerns over privacy and data misuse.This kind of distrust prevents the economic value of data from being fully released since users are unwilling to share their data and platforms are abusing their power.It further hinders the high-quality development of digital economy.Moreover,data utilization by nature may induce negative social externalities,for example,resulting in social injustice by discrimination towards minority in data-intensive AIs,and data leakage can endanger national security.

To solve the aforementioned problems,this paper investigates the development of data collection and utilization in different development stages of digital economy.Following the setup in Fainmesser et al.(2022),our model includes one platform and a measure-one consumer.The information the platform collected improves the service it provides to consumers,but also incurs private privacy cost and social cost due to negative information externalities.We focus on the usage data,which produced by user activity.In Stage I,no data is collected nor utilized since lacking of technologies,as in the stage of web 1.0.In Stage II,big data can be processed and brings profit for platform,who collect data from consumers under opaque data policy,which assembles digital economy without effective supervision,like the current stage of web 2.0.In Stage III,platform’s data collection and usage are under strict supervision,thus allows transparent and credible data policy,implying that digital trust networks are built.Stage III represents well-supervised digital economy now and in the future,which becomes an important development direction of web 3.0.The main conclusions are as follows: ①The digital trust,or say,transparent and credible data policy,enhances the platform’s profitability by enabling the platform to commit to collect less proportion of user’s data,making the data collection choice for the platform flexible.This reduces the marginal privacy cost of usage for user,thus enhancing the user’s activity.Contrary to common intuition,opaque and inability to credibly commit constrain the platform’s profitability.②Even with strong supervision to ensure credible data policy,social externalities of data utilization may still undermine social welfare in stage III.The key lies in the social cost of data.Since in stage III,mature digital trust networks may increase the amount of data collected by platforms due to higher user’s activity,thus higher social cost can hurt total consumer welfare,and subsequently social welfare when the negative externalities of data are severe.This coincides with the data collection in practice.According to the World Economic Forum’s End User Perspectives on Digital Media Survey 2017,transparency in data policy is considered one of the most important ways to improve digital trust,and the lack of digital trust has caused 70% of Chinese respondents to avoid or stop using a digital service.At the same time,Accenture Consulting Company’s 2017 Global Consumer Dynamics Survey also pointed out that 62% of respondents want companies to use information more openly and transparently to win consumer trust,while imperfect personalized experiences and lack of digital trust have cost Chinese companies up to 5.3 trillion yuan in revenue over the past year.

As for policy implications,this paper argues that mature digital trust networks are essential to solve the problems arising from the intractable data collection and utilization activities of platforms.However,it cannot completely eliminate negative social externalities of data.Strict constraints on the proportion of users’ data collected are also required to improve total social welfare.This corresponds to the principle of minimal and necessity brought up by Personal Information Protection Law,which aims to set the upper bound of collection frequency,data type,data authority,the amount of collected data and etc.While our study stresses that such constraints must be applied in the digital economy with mature digital trust networks,lacking of digital trust makes these efforts in vein.Moreover,we suggest more measures to make users easier to understand and monitor the data collection and utilization behavior of the platform.For example,there can be an integrated index for users to compare the degree of data collection and utilization and related potential risk of softwares,so that users do not need to go over the lengthy and complex data policy contract.

The main innovation of this paper lies in two aspects.On the one hand,this is the first paper illustrating the digital development of platform economy with the development of web,highlighting the role of digital trust in shaping the platform’s profitability.Surprisingly,we find the transparency principle to enhance digital trust improves platform’s profitability by encouraging more user’s activity.On the other hand,we first show that the constraint on platform’s data collection behavior to boost the prosperity of digital economy can’t work without the mature digital trust networks.

This paper uses a stylized duopoly model to study the competitive effects of Behavior-Based Servicing (BBS), a common practice that rewards loyal customers with service priority. BBS entails dual externalities: improved service quality to high-priority customers (positive externality) leads to reduced service quality to low-priority customers (negative externality), and vice versa. When both firms adopt BBS, their customers may pay a status premium above their natural product valuation in anticipation of future value from the positive externality. Both firms gain profits in such a status-seeking equilibrium. However, consumers can become apathetic to BBS in a market with a strong negative externality for low-priority customers; in such an equilibrium, loyal customers do not benefit from their high-priority status. When competing firms pursue opposite BBS strategies, the firm which does not use BBS can earn a higher profit than its competitor because new customers will experience the negative externality only from the firm that uses BBS. These results underscore the importance of examining both sides of externalities engendered by BBS. 

How to attract participation and elicit contributions in charity-based online crowd-funding has remained a challenging problem for a long time. Leveraging on corporate or individual funds to set up a matching fund mechanism has been widely adopted as a popular approach to motivate lenders’ donations. This study examines how the matching fund mechanism affects individual lenders’ contribution, theoretically and empirically. On the one hand, the classical tenet of public economics claims that each individual relies on peers to contribute more and enjoy the project. Hence, an outsider’s contribution via the matching fund mechanism should decrease individual lenders’ contribution due to the “free-riding” problem. On the other hand, the “strategic complements across time” effect in the framework of dynamic provision of public goods results in more contributions from individual lenders. Our theoretical model illustrates that which of the two opposing effects dominates hinges crucially on the intrinsic quality of a project. When the endogenous completion time of a project is short, it has high intrinsic quality and getting matched is more likely to crowd-in individual lenders’ contribution. Otherwise, for a project with low intrinsic quality, getting matched may crowd-out individual lenders’ contribution. Using real-time data from Kiva.org, a well-known charity-based crowd-funding platform, we find that the empirical results provide supportive evidence for the theoretical predictions. 

With the largest foreign reserves and the third-largest weight in the SDR basket,China's exchange rate policy has become increasingly important in international trade and the global financial market.In this paper,we assess the impact of reforms of the RMB exchange rate regime on the off-shore FX market efficiency,from the perspective of deviations from covered interest rate parity (CIP).In particular,we examine the magnitude of CIP deviations as well as what drives these deviations under different policy regimes.

Given the relatively strict capital controls in China,we focus on the off-shore RMB and calculate the CIP deviations with respect to five major currencies—the US dollar,the euro,the Japanese yen,the Australian dollar,and the British pound—over the 2011—2022 period.We find that the CIP deviations of the RMB are on average positive.As part of the RMB internationalization effort,the PBC changed the RMB/USD central parity quoting mechanism on August 11, 2015,with the intention of enhancing the market determination of  the RMB exchange rate.We take this “8·11” reform as the cut-off and examine the level and determinants of CIP deviations before and after this event.

We find that the CIP deviations are significantly lower in the period after the “8·11”  reform relative to that before the reform.This decline in CIP deviations is mainly driven by the market-oriented policy regimes rather than the regulation-oriented regimes,indicating that the market-oriented exchange rate reform improves the efficiency of the foreign exchange market.In addition,we document two economic mechanisms through which the exchange rate reform influences CIP deviations.First,the exchange rate reform promotes price liberalization,tightening the interest rate parity.Second,the reform reduces market friction and uncertainty associated with government interventions.

This paper further examines the potential determinants of CIP deviations at daily and monthly frequencies,which are grouped into three categories: limits to arbitrage,market risk,and supply/demand factors.In the full sample,the impact of those variables on CIP deviations is consistent with previous literature.We find that before the exchange rate reform,all those three groups of variables influenced  CIP deviations,and the variables in the group of limits to arbitrage had the most significant impact.However,after the exchange rate reform,the influence of limits to arbitrage related variables was significantly weakened.The influence of market risk-related variables also decreases,but the impact of supply-demand related variables remains unchanged after the exchange rate reform.This is consistent with the assumption that market-oriented exchange rate reform increases the market efficiency of the RMB exchange rate.The exchange rate reform does not only decrease the absolute level of CIP deviation,but also reduces the impact of limits to arbitrage related and market risk related factors through a more market-oriented exchange rate.It indicates that those market frictions that cause CIP deviation may be partially alleviated.The impact of supply and demand factors may not be altered by the reduced market frictions.

The offshore market serves as a crucial platform for cross-border transactions and the internationalization of the RMB.The findings of this paper contribute to the understanding of the impact of exchange rate reform on the effectiveness of the offshore market,and shed light on policies for promoting RMB internationalization and enhancing the openness of the financial market.

This paper presents several key policy implications.First,it endorses a progressive and prudent approach to market-led exchange rate reform,which entails increasing market involvement in setting exchange rates to deepen and diversify the market.Our research suggests these reforms not only boost offshore market efficiency but also allow exchange rates to more precisely reflect the dynamics of supply and demand,which is crucial for the internationalization of the RMB.Second,the study underscores the need to grow and improve the integration of financial markets.The noted differences in Covered Interest Parity (CIP) between offshore and onshore markets underscore the importance of a more integrated financial market to address trading barriers and speculative risks.Strengthening connections in the financial market is essential for bolstering the RMB's global presence and liquidity.Finally,the paper stresses the continuous need for macro-prudential regulation to maintain a stable macroeconomic setting.Factors such as short-term capital flows,interest rates,and market volatility have significant impacts on the efficacy of offshore markets.The paper concludes by recommending heightened oversight,enhanced risk management,and greater transparency in the exchange rate market to reduce financial risks and promote stability.

With the rapid development of big data technology as well as the popularization of third-party payment services,more and more transactions are conducted digitally and recorded in databases.The massive transaction data,which contain merchants' behavior logs,can serve as a valuable resource for mining behavioral information merchants.Segmenting merchants into different groups according to their behavior patterns contributes to precise and personalized decision-supports for marketing,risk control,and many other management issues related to merchants.This is of great importance to the management and revenue of third-party payment platforms,as well as promoting the sustainable development of the real economy.With respect to the segmentation of individuals using transaction data,previous methods are typically based on feature engineering.In this way,a large amount of transaction records are compressed into low-dimensional dense feature vectors.Then,with merchants presented by feature vectors,clustering methods are implemented on those vectors to output partition for all merchants.However,feature-based methods have limited efficiency in utilizing the data and inevitably lead to information loss,which may greatly restrict the effectiveness of the segmentation results.

To make full use of transaction data,in this paper,we investigate the empirical distributions of transactions for a better understanding of merchants' behaviors.Compared with low-dimensional feature vectors,the empirical distributions of transactions are much more informative.Nevertheless,how to conduct clustering analysis based on empirical distributions is a challenging task.Traditional clustering algorithms,which are typically applicable for structured data,can hardly be directly used for empirical distributions.To fix this problem,this paper proposes a novel clustering algorithm for merchant segmentation based on multivariate distribution functions among transactions.Firstly,the Gaussian Mixture Model (GMM) is adopted to fit the distribution among the whole dataset.The combinations of Gaussian components within GMM are utilized to describe meaningful patterns of transaction behaviors.With all transactions modelled by GMM,the relations between a transaction and the Gaussian components are simultaneously estimated.As a result,the relations between a merchant and the Gaussian components can be thus inferred via aggregating results of corresponding transactions.Secondly,based on the estimation of GMM,the Wasserstein distance is exploited to measure the dissimilarities among merchants' distributions.Specifically,we apply sliced Wasserstein distance for the purpose of the computational efficiency.Finally,we develop an iterative algorithm,which is called K-means Clustering algorithm based on GMM and Wasserstein Distance (GWKC),to cluster all merchants according to the dissimilarities among their distributions.With the empirical distributions among transactions fully taken into consideration,our method provides a reasonable solution for the segmentation of merchants.In regard to the hyperparameters of our method,we also provide information criterion as reference for real applications.

The GWKC algorithm mentioned above utilizes the differences in transaction distribution among merchants for clustering.To further improve the clustering performance,this paper considers integrating more transaction-related covariate features to boost the GWKC algorithm.These covariate features,e.g.,average transaction amount,average number of transactions,and suspected cash-out transactions,serve as supplementary information to assist and adjust the results of GWKC.The improved clustering algorithm is called GWKC With Weighted Covariates (GWKC+WCov) in this paper.This version covers information on both feature vectors and empirical distributions.It allows the integration of distribution-based clustering methods with feature engineering,incorporating highly personalized and complex features that involve expert experience and business knowledge into the clustering process.It is noteworthy that when integrating distribution and covariates to measure the differences among merchants,it is necessary to determine the weights of different parts,e.g.the measurement based on distributions and those based on feature vectors.To obtain appropriate weights,this paper proposes an adaptive approach to iteratively search for weights that optimize the clustering performance.Thus,GWKC+WCov is able to integrate multiple structural features for comprehensive clustering.

Both simulation and real data analysis show that the proposed algorithm significantly outperforms previous methods.With structural information of distributions involved,GWKC performs much better than those based on feature vectors.Moreover,the visualization of the results of GWKC intuitively illustrates  the behavior patterns of cash-out merchants,thus providing decision-making supports for risk detection and differentiated management of payment platforms.Among various methods,GWKC+WCov achieves the best performance.Since it adaptively integrates multiple structural information within transactions,it is supposed to be a promising solution for real applications of merchant segmentation.

Possible directions for future works are also discussed in this paper.Firstly,the proposed methods can be further extended through integrating more unstructured data,e.g.,network data or text data.Thus,the clustering results may be more informative with more available data sources incorporated.Secondly,in regard to the modelling of empirical distributions,it is possible to apply different finite mixture models.For datasets with arbitrary distributions,non-Gaussian components,e.g.,Gamma components,or nonparametric estimations may also be useful.Based on the proposed methods,we can derive more flexible versions to further optimize the clustering performance.

Small and medium-sized enterprises (SMEs) have made important contributions to China's economic development.However,SMEs generally exhibit weak resilience and are constrained by limited capacities of financing and risk sharing.When facing negative economic shocks,they often lack the resources and capabilities to adjust and alleviate difficulties.Therefore,what challenges will SMEs face under negative economic shocks?How will these challenges hinder SMEs from recovery and growth?What adjustments will they make to cope with these challenges?How will negative economic shocks affect the development of SMEs in the medium and long term?Answering these questions not only holds clear theoretical significance but also practical significance and policy implications,especially today when many SMEs have not fully recovered from the negative economic impacts of the COVID-19 pandemic.

Based on data from two rounds of the Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) conducted in 2018 and 2023,we attempt to answer the series of questions raised above using the COVID-19 pandemic as a typical negative economic shock.The ESIEC is a field survey conducted by Peking University on private enterprises in China.In 2018,the research team conducted the first baseline survey,covering 6198 firms and accumulating quite detailed enterprise data.In 2023,the research team conducted the second large-scale field survey,completing on-site surveys for 6117 enterprises and investigating the challenges encountered by enterprises during the pandemic and their responses.The two rounds of surveys happened to span the COVID-19 pandemic,providing us with an opportunity to systematically evaluate the impacts of negative economic shocks on SMEs,the underlying mechanisms,and to detail the adjustments made by SMEs to cope with these shocks.
In this paper,we employ a “quasi-difference-in-differences” model to examine the challenges faced by SMEs under the impact of the COVID-19 pandemic,as well as the strategies they adopted in response and adjustment,taking advantage of the variation in strength of pandemic control measures across regions and over time.We find that:① The pandemic influences the supply side of SMEs mainly due to the “shutdown” effect caused by supply chain disruptions.②  To cope with the impact of the pandemic on the supply side and strengthen the resilience of the supply chain,SMEs need to systematically adjust the size and spatial layout of  the supplier network,but at the same time bearing higher transportation and transaction costs.This effect is more pronounced in regions with lower industrial agglomeration.③ The pandemic affects the demand side of SMEs mainly due to the “contraction” effect brought about by market shrinkage.④ To cope with the impact of the pandemic on the demand side,SMEs need to further explore customer resources,strengthen innovation efforts,and adjust business models to ensure market size and increase profitability.Whether these adjustments can be made in a timely and effective manner is highly correlated with the resources and backgrounds of the enterprises and entrepreneurs.⑤ The pandemic has significantly negative effects on revenue and gross profit,and may lead to a “scarring effect” delaying the recovery and development of SMEs.

These findings contribute to our understanding of the key mechanisms for the recovery of SMEs in the post-pandemic era.They also provide theoretical insights and empirical evidence for the targeted design of policies to assist SMEs,facilitating an efficient and balanced economic recovery.Specifically,the  “scarring effect” of the negative economic shock like the COVID-19 pandemic may hinder the development of SMEs in the medium and long term not only due to the persistent impacts of the  “shutdown”and  “contraction”effects but also largely due to a series of adjustments made by enterprises in response to the shock.Policymakers should consider aligning relief policies with the adjustments made by enterprises themselves to amplify policy impacts,thereby effectively promoting and accelerating the recovery of enterprises and the economy.At the same time,negative economic shocks often lead to a  “reshuffling”of the market landscape.Enterprises that can adjust promptly and effectively,and even capitalize on opportunities to upgrade,often gain greater competitive advantages and market share after the shock,potentially leading to changes in market structure and even widening regional disparities.Therefore,in policy design for the post-pandemic era,it is also necessary to fully consider the balance between enterprises and between regions,thus achieving economic recovery efficiently and equitably.

The paper examines the impact of the “Double Reduction” policy implemented in 2021 on the academic burden,family education expenditure,and physical and mental health of parents/students in primary and secondary schools.The excessive academic burden has raised concerns in society and academia.The rise of the extracurricular education industry and educational competition may have worsened the situation,leading to increased stress on students and parental anxiety.This exam-oriented approach and the focus on further education not only burden families financially but also hinder students’ physical and mental development,stifling creativity and innovation necessary for future progress.Previous policies aimed at reducing educational burden primarily focused on decreasing workload within schools,but studies found that parents often compensated by increasing spending on extracurricular education,and this intensified competition disproportionately affect students from low-income families and rural areas.To address these issues,the“Double Reduction” policy was introduced on July 242021,aiming to effectively reduce academic workload,off-campus training burden,family educational expenses,and parental effort.It imposes strict regulations on both in-school learning and off-campus tutoring,focusing on curbing excessive competition and promoting a balanced educational resource distribution.

We conducted a nationwide survey covering approximately 2 000 primary and secondary school parents from 29 provinces across the country to examine the effects of the “Double Reduction” policy.The survey collected detailed information on students and families during the two semesters,before and after the implementation of the policy.The questionnaire was designed in line with the guiding principles and objectives of the policy,consisting of three main sections:the reduction of academic burden within and outsideschool (detailed activities of students during school hours,after-school study,and off-campus tutoring),family educational investment (financial and time investment in various educational activities),and the physical and mental health of parents and students,as well as their subjective perceptions and beliefs.Using the survey data,we employed an individual fixed effects model and a generalized difference-in-differences model based on policy intensityto compare the two semesters before and after the implementation of the “Double Reduction” policy.

We find that after the policy implementation,the average total duration of students’ after-school study decreased by approximately one-fourth,family educational expenditure and parental time investment decreased by about 15%,and there were significant improvements in parental stress,students’physical and mental health,learning initiative,and parent-child relationships.These effects exhibited considerable heterogeneity across different households.Compared to families with parents holding graduate degrees,families with parents with undergraduate degrees or below experienced a more pronounced reduction in the burden of educational expenditure,and these parents and students also experienced relatively greater improvements in their physical and mental health.Further exploration of parental attitudes revealed that parents generally believed that the overall impact of the “Double Reduction” policy on their children was positive.Moreover,parents with more positive views towards the policy and those who believed that other families would also reduce their competitive educational investment simultaneously tended to reduce their educational burden to a greater extent.This suggests that parental decisions regarding educational investment are largely strategic responses to cope with the educational decisions of other families in a competitive environment,resembling the prisoner’s dilemmain educational investment.

It should be emphasized that depression is now widespread among Chinese adolescents,as well as the anxiety and pressure experienced by parents.Therefore,variables related to the physical and mental health of students and parents are crucial dimensions for policy evaluation,which have not received sufficient attention in previous literature.The findings of this analysis indicate that the “Double Reduction” policy has significant positive effects on improving the physical and mental health of parents and children.These findings contribute to a timely and comprehensive understanding of the multifaceted impacts of the “Double Reduction” policy,providing support for the continued advancement and optimization of educational policies.

This study is the first comprehensive quantitative analysis of the effects of the “Double Reduction” policy using household-level data on educational investment and behaviors.Existing research has mainly focused on the reduction of academic burden within schools prior to 2021.However,the uniqueness of the “Double Reduction” policy lies in its simultaneous restriction of both in-school education supply and off-school tutoring services.Until now,there has been very limited research on the effects of the “Double Reduction” policy.This study,based on detailed micro-level survey data covering two academic semesters before and after the policy implementation,can more directly and causally quantify the effects of the “Double Reduction” policy,effectively filling the gap in existing research.These rigorous and timely evaluations of the effects of the “Double Reduction” policy can provide references for the continued policy refinements in educational burden reduction and the establishment of long-term instituitional arrangement.

The interaction between the digital revolution and climate change has prompted significant interest in understanding the role of digitalization in promoting low-carbon development. As scholars both domestically and internationally investigate whether digital technologies can effectively reduce carbon emissions,the discourse has evolved into a critical area of inquiry. This paper synthesizes existing literature on the carbon emission effects of digitalization,presenting a structured analysis across three dimensions:theoretical frameworks,mechanism analyses,and empirical validations.

Digitalization is characterized as a general-purpose technology,differentiating it from traditional technologies through its systemic,multi-layered,and structural impacts on the economic environment. Such characteristics signify that the effects of digitalization on carbon emissions extend beyond conventional analytical frameworks of technological economics. The neo-classical economic growth model,which has historically served as a theoretical basis for understanding economic interactions,appears increasingly inadequate in fully elucidating the complex dynamics between digital technology and carbon emissions. This inadequacy highlights the necessity for a reevaluation of the theoretical underpinnings that inform research on the environmental impacts of digitization,signaling it as a prominent frontier in scholarly inquiry.

In exploring these impacts,researchers have turned to multi-level analytical approaches,uncovering various mechanisms that elucidate the interplay between digitalization and environmental outcomes. Noteworthy among these mechanisms are several critical effects:the substitution and income effects of digitalization on energy consumption,which imply that the introduction of digital technologies can lead to improved efficiencies and altered consumption behaviors;the efficiency effects of digital technologies on energy technologies themselves,fostering advancements in clean energy solutions;the transformation effects that accompany digitalization,prompting shifts in economic structures towards more sustainable practices;and enabling effects that enhance the capacity of individuals and organizations to engage in environmentally friendly behaviors.

The complexity inherent in these different mechanisms yields diverse outcomes,accentuating the need for rigorous empirical testing of the relationship between digitalization and carbon emissions. Thus,this area of research has garnered attention,as evidenced by the substantial body of literature employing econometric methods to explore these relationships. Predominantly,studies within this domain leverage a variety of samples,timeframes,and methodologies to analyze how digitization influences carbon emissions and sustainability efforts. Nevertheless,perspectives on the ability of digitalization to facilitate a transition towards green,low-carbon economies remain varied and sometimes contentious.

Contemporary empirical research has increasingly gravitated toward direct estimations of carbon emissions linked to digital development,particularly focusing on the concept of implicit carbon through environmental assessment paradigms. This methodological shift signifies an evolving understanding of the ecological footprint associated with the proliferation of digital technologies. Within the context of China,research has predominantly centered on testing the relationship between digitization and carbon emissions,revealing that nearly 94% of empirical studies support the assertion that digitalization can contribute to carbon reduction. Such contributions are typically framed in  promoting technological innovation and fostering structural transformation within industries.

However,despite this supportive empirical evidence,there remains a notable gap in understanding the theoretical foundations that underpin the positive correlations between digitalization and carbon reduction. This lack of theoretical engagement raises concerns regarding the potential overestimation of the positive impacts attributed to digitalization. As a developing country,China's experience offers unique insights,especially given the continuing expansion of its industrial economy alongside the rapid advancement of its digital economy. Notably,while digital technologies are swiftly integrating into traditional economic sectors,the country's energy system has not yet achieved a predominant transition towards renewable energy sources,complicating the narrative of digitization's impact on sustainability.

The actual effects of digital development on carbon emissions may diverge from findings observed in empirical studies,indicating a need for further investigation into this dynamic. As such,the article advocates for an enhanced emphasis on the theoretical exploration of carbon emission effects related to digitalization. It underscores the importance of comprehensively understanding the specific mechanisms through which digitalization influences carbon emissions to provide a more nuanced perspective on its potential benefits.

Furthermore,this discourse encourages researchers to conduct more targeted assessments of digitalization's carbon emission effects,particularly in relation to its application within various industries in China. As digital technologies continue to evolve,policymakers and stakeholders must acquire a holistic and objective understanding of the carbon reduction effects attributed to digitalization. By doing so,strategic decisions can be made that leverage digital advancements to foster economic growth while simultaneously addressing pressing environmental challenges.

Overall,the intersection of digitalization and climate change remains a fertile ground for academic exploration,necessitating a balanced integration of theoretical and empirical approaches to elucidate the complex relationships at play. Understanding these dynamics not only aids in effective policymaking but also contributes to the broader discourse on sustainability and climate resilience in the face of ongoing technological advancements.

How do organizations adapt to environmental change? Borrowing the concept of Ti (essence) and Xiang (faces) from Buddhism and building on the case analysis of Wangsu, we identify the elements of stability and change. The stable elements are established during the process of the firm’s founding and development, including identity, business model and core competency, which compose the Ti, while the Xiang are products or services the firm chooses as an adaptation to the environment. The Ti affects Xiang through enabling and constraining, while the change of Xiang provides feedback to the Ti through reinforcement, enrichment, and adjustment. One Ti multiple Xiang captures the general rule of organizational adaptation. 

We analyze how informed investors can learn from each other through disclosed trades. We show that disclosure always increases market efficiency but its effect on informed investors' profits is ambiguous. When informed investors have highly complementary signals, disclosure makes them coordinate their trades, so their expected profits are higher. Moreover, an informed investor with very imprecise information prefers competition in the presence of disclosure as they learns more from the other informed investors than the market and makes more profits than they would obtain if they is the only informed investors. There could exist herding when information acquisition is endogenous. 

Based on the theory of growth at risk,this papermeasures the economicdownside risk for each city in China,and empirically examines the impact of local government debt on the economic downturn risk under the background of the implementation of the new “budget law”.The result shows that the increase of local government debt can reduce the economic downside risk in the short term,while it will aggravate the economic downside risk in the medium and long term.This result remains robust not only by using different measures of local government debt and economic downside risk but also by taking spatial spillover effects and endogenous  situationsinto consideration.Moreover,after classifying the samples according to the level of economic marketization,this paper further finds that compared with the regions with a lower degree of economic marketization,the increase of local government debt will significantly reduce the economic downside risk in the short-term and significantly increase the economic downside risk in the long term in the regions with higher local government debt.And so is the increase of local government debt in the regions with larger economic downside risk.This paper further finds that local government debt will affect the economic downturn risk through three mechanisms,namely the investment crowding out mechanism,the credit crowding out mechanism and the innovation suppression mechanism.

Compared with the existing literature,this paper may have two marginal contributions.First,unlike most of the existing literature on economic downside risks focusing on the measurement and prediction of economic downside risks,this paper examines the causal relationship between the local government debt and the macroeconomic downside risks,whichenriches the literature on macroeconomic downside risks.Second,unlike most of the existing literature on local government debt focusing on the short-term economic effects of local government debt,this paper examines both the short-term and long-term economic effects of local government debt from an inter-temporal perspective,which broadens the research perspective on local government debt.

This paper not only provides a new perspective on the causal inference of economic downturn risks,but also provides a reference for effectively preventing systemic financial risks and local government debt risks and maintaining high-quality economic development.According to the conclusions,this paper makes several suggestions as follows.First,to control the scale of local government debt from the source and prevent systemic financial risks caused by localgovernment debt risks,the local governments should further establish the local government debt management system,improve the local government debt information disclosure system,actively implement the local government debt supervision and early warning mechanism,and strictly implement debt investment and financing decision-making mechanism.Second,the local governments should fully recognize the dual influence of the local government debt that the local government debt could be raised to improve the ability of local governments to regulate the economy,promote the rapid development of urban public infrastructure,and stimulate the stable growth of the local economy on one hand and the local government debt would bring negative impacts in the long term on the other hand.Last but not least,local governments should give more rights to micro-market entities,avoid excessive crowding out of enterprise resources,promote better allocation of market resources,minimize the negative consequence of local government debt on the macroeconomy,and constantly optimize the construction of macro-economic governance system,so as to ultimately obtain the high-quality economic development.

This paper proposes a parsimonious approach to estimate the effect of social interaction on stock market participation. Using data publicly available, we construct a sequence of measures of aggregated stock returns that are based on the same stock returns information and embed social interaction increasingly through different weighting functions. We show that the explanatory power over stock market participation is increasing in this sequence of aggregated stock returns measures. The effect is stronger when social interaction is stronger or social communication costs are lower, when the social information is positive and during bull markets. Our approach potentially is applicable to study the effects of social interactions in the aggregate in a wide range of contexts.

As Internet, Internet of Things (IoT), and Artificial Intelligence (AI) technologies rapidly evolve, data has become a critical driving force behind economic and technological advancement. Companies can leverage data analysis to gain comprehensive insights into customer behavior, market trends, and operational performance, thereby making informed decisions and enhancing overall performance. However, a single organization’s data may not be sufficient for comprehensive data analysis, posing a significant challenge. For instance, developing an accurate marketing model to target users may necessitate data from multiple sources, such as telecom operators, social networking sites, and e-commerce platforms. This data scarcity necessitates data-sharing mechanisms, which are often fraught with concerns surround data privacy,ethics,and legality. In this regard, Federated Learning (FL)—a novel machine learning paradigm—has garnered increasing attention. FL participants can train local models, safeguard data privacy, and exchange only model parameters with servers or other peers, fully capitalizing on the value of data. This “data-available-but-not-visible” approach is gaining popularity in data-intensive fields.

Many FL tasks cannot be accomplished in a single instance and require sustained collaboration among multiple parties. For example, in the joint development of an FL model across multiple medical institutions to detect and manage chronic diseases, continuous accumulation of clinical data, learning from case changes, and model robustness and predictability improvements are necessary to reflect the latest medical knowledge and practices. Current literature on FL cooperative behavior and incentive mechanisms,however,primarily focuses on cross-device federated learning and considers only one-off cooperation. This modeling is inadequate for characterizing practical cross-silo long-term FL patterns. On the one hand, cross-silo FL participants, who also accumulate a certain amount of data,have more complex and diverse strategic options compared to those in cross-device FL.Participants can choose to participate in public training or solely improve their model utility through local training.On the other hand,when cooperation transitions from a one-off to a long-term scenario,time inconsistency issues may lead to free-riding behaviors,incentivizing participants to delay data contributions while enjoying the benefits of others’ contributions.To address these limitations,this study concentrates on the long-term cross-silo FL process,establishing a dynamic game model to characterize federated clients’ interactive strategies and proposing a reinforcement learning-based incentive mechanism to encourage rational participant contribution,aiming to boost the FL system’s overall revenue.

This paper first establishes a dynamic game model to characterize federated clients’ long-term interactive strategies.We devise a cooperation contract in which the central server only transmits the aggregated parameters to current training period contributors.With the long-term cross-silo FL cooperation process divided into several model training periods,clients have two strategic choices in each period:to participate in public federated training or to retain data for local training only.At the end of  each period,clients receive feedback parameters from the central server and gain corresponding benefits based on their local models’ accuracy.In this framework,clients face a trade-off between participation costs and potential early contribution benefits.Given the information accumulation in the model with the client’s input,clients also confront a cross-period decision-making problem regarding resource allocation throughout the entire long-term FL cooperation process.Based on these background assumptions,this paper establishes a game tree to consider the game solution,where clients’ decisions in each training period are based on full knowledge of past cooperation and rational expectations of future actions.Through backward induction,we solve for the client’s equilibrium strategy,which exhibits intermittent contribution gaps,clearly deviating from the socially optimal cooperative pattern.

Building on the above game analysis,this paper subsequently designs a dynamic incentive scheme based on reinforcement learning,setting incentives for different training periods based on clients’ cooperation progress.Firstly,we regard the FL organization as a central planner responsible for issuing incentives before each training period to encourage federated client input.The Deep Reinforcement Learning (DRL) agent assists the central planner in making incentive decisions,with federated clients serving as the environment with which the agent interacts.On the one hand,we meticulously design the state,action,and reward of the DRL method to fully encompass the information of the federated learning cooperation process.On the other hand,we introduce enhancements to the traditional Deep Q-Network (DQN) method,such as Double Deep Q-Network (DDQN),prioritized replay,and noisy network,to augment the method’s performance.Through extensive experiments,we verify the scheme’s effectiveness in improving the system’s total revenue and controlling incentive costs.Reasonable incentive cost penalties can guide the DRL agent towards the most cost-effective incentive scheme,accurately incentivizing low-willingness cooperation periods of clients,and the system revenue under the same budget significantly surpasses that of fixed incentives.

This paper not only theoretically uncovers the dynamic patterns in long-term cross-silo federated learning cooperation but also proposes innovative incentive mechanisms to enhance cooperation efficiency,offering fresh insights and methodologies for effectively facilitating data sharing and cooperation in the contemporary information era.

With the continuous evolution of the global value chain (GVC) over time,China’s strategic emerging industries have been deeply embedded in the global value chain.By integrating into global innovation networks,strategic emerging industries achieve continuous evolution and development.Under the current pressure of green transformation,it has become inevitable to cultivate and develop green and environmentally friendly strategic emerging industries.Hence,as the global division of labor becomes more pronounced,the integration of China’s strategic emerging industries into the GVC will undeniably influence their green technological innovation.However,there is still little literature linking the green technology innovation of enterprises with the GVC participation of their industries.Given that enterprises are the main subjects of green transformation implementation,we argue that it is necessary to focus on enterprise green technology innovation.In addition,few studies have considered the unique characteristics and strategic significance of strategic emerging industries when examining corporate green technology innovation within these industries.

Based on data from 1476 samples of Chinese strategic emerging industries,this paper constructs a fixed-effects model using enterprise data.We explore the influence mechanism of GVC participation in Chinese strategic emerging industries on firms’ green technology innovation.The results reveal that： ① the GVC participation position of strategic emerging industries significantly and positively affects the green technology innovation performance of enterprises; ② backward participation plays a negative mediating utility in the GVC participation position and enterprises’ green technology innovation;  ③ the regional macroeconomic level positively regulates the relationship between GVC participation position and green technology innovation.

According to the above research results,this paper gets the following insights.First,the government must recognize the importance of the industry’s GVC embedding status for enterprises’ green technology innovation,and actively promote the development of strategic emerging industries.Secondly,the development of green technology cannot be separated from economic support.Therefore,the government can combine the development of traditional industries and strategic emerging industries,using new technologies to empower traditional industries to achieve economic growth.At the same time,the government can also formulate multi-stage and multi-type policies to encourage the re-research and development of green technology innovation.Third,enterprises should maintain an aggressive attitude when participating in the global division of labor.Enterprises should establish a network of trustworthy relationships with other enterprises,strengthen their sense of independent innovation,and learn in the process of cooperation.By strengthening enterprises’ sense of independent innovation and learning,we can promote the benign development of the industry from the enterprise perspective and get rid of the dilemma of being trapped at the bottom of the value chain.

This paper enriches the study of economic transformation of strategic emerging industries from the enterprise perspective.It not only provides a new perspective for the study of value chain theory and green technology innovation,but also provides theoretical reference and guidance for the practice of green innovation in other industries.

This paper explores academic entrepreneurship within the context of Chinese universities and research institutes,where the interrelationships among major institutional logics present a contrasting setting to the western context.In prevalent western scenarios,the science community has long been bound by the Mertonian norm of disinterestedness and communism,which encourages the free pursuit of knowledge and avoids direct commercial activity.As such,a significant issue when commercializing science is to increase legitimacy and redefine the boundary between academia and commerce.
The evolution of the Science & Technology system in China has undergone a substantial transformation in institutional logics.Prior to market reform,the state logic was dominant within Chinese universities and research institutes concerning all aspects of resource allocation,output goals,and human resource management.Universities and research institutes,under direct government control,functioned as instruments for training talent and providing technologies according to government demands.Therefore,technology transfer and utilization were legitimate within universities and research institutes from the onset.Scientists were allowed and instructed to apply their research outputs,albeit weakly connected to individual economic interests.Later,during the economic reform era,the state progressively shifted its administration role into macro guidance and oversight.Under state guidance,both professional logic and market logic emerged and prevailed within universities and research institutes.The market logic supplanted the state’s role in promoting the utilization of scientific outputs,further reconstructing the incentive system and utilization mechanisms towards commercialization.This change invigorated the economic incentives for academic scientists,requiring them to gain more knowledge and exposure to industrial activities to understand enterprises’ technological needs. Meanwhile,professional logic rose to take over governance within the academic community.With higher education reforms,contract systems and performance-driven reward systems gradually replaced permanent employment and fixed salaries in universities and research institutes.In relation to the state’s role,both the central and local governments assumed macro-level supervision responsibilities such as legislation,policymaking,and funding,while refraining from intervening in governance within individual universities or research institutes.

The unique interrelationships among state,market,and professional logics in the context of the Chinese science sector pave the way for new research agendas of academic entrepreneurship.The commercialization of science inherits legitimacy from the state planning period,when scientific discoveries were directed to be pragmatic and applicable,and further stabilizes it during the reform period.However,whether academic scientists can fully realize their potential to commercialize research outputs is unclear,as the co-existing state,market,and professional logics prescribe differing expectations for their behaviors.To solve this puzzle,this study combines the institutional logics perspective with identity control theory,demonstrating how academic scientists develop entrepreneurial or academic identities through an identity control process which links identity formation with each institutional logic’s expectations.The study proposes that institutional logics provide the guidelines and standards for forming academic and entrepreneurial identities.Scientists then verify their congruity or incongruity with each identity,guiding their commercialization activity.

This study utilized data from the third wave of the National Survey on Scientific and Technical Professionals in China,conducted by the China Association for Science and Technology (CAST) in 2013.By analyzing a representative sample of 1776 academic scientists in Beijing,the findings suggest that market logic has a direct effect on scientists,encouraging them to foster an entrepreneurial identity and to actively work towards commercializing their potentially valuable research achievements.Specifically,a scientist’s patenting ability is positively associated with the rate of research achievement commercialization.Furthermore,the influence of market logic is contingent on the impact of professional and state logics.Professional logic prescribes the standard for academic identity,while state logic sets expectations for both academic and entrepreneurial identities.In accordance with professional logic,satisfaction with the professional title appraisal system strengthens the academic identity,thus weakening the positive relationship between a scientist’s patenting ability and commercialization activity.

State logic exerts a dual effect on the impact of market logic:Facilitation by the state in strengthening entrepreneurial identity enhances the effect of market logic,while the intensification of academic identity as a result of professional or state influence diminishes the effect of market logic.The results suggest that academic scientists who receive more public funds from central and local governments are less engaged in realizing their potential for commercialization,while those who perceive more support from national Science and Technology policies are more likely to do so.These findings illuminate the mechanisms through which institutional logics influence individual behavior and deepen the understanding of commercialization activity in Chinese universities and research institutes.

This study contributes to both the academic entrepreneurship and institutional logics literature.First,it examines academic entrepreneurship in the relatively unexplored context of Chinese universities and research institutes,where institutional settings are markedly different from their western counterparts due to divergent historical trajectories.By introducing a new context in which the academic norm is not in contradiction with commerce,the study contributes to a new research agenda focused on scientists’ engagement in commercialization activity in the face of co-existing,yet not conflicting,logics.Second,by integrating institutional logics with identity control theory,the theoretical framework of this study provides a microfoundation for the gap between institutional logics and individual action.The proposed identity process also provides insights into embedded agency at the individual level,emphasizing the role of human agency.This suggests that individual actors could leverage co-existing institutional logics and manage their identities to optimize their career outcomes.

Sentencing is the ultimate embodiment of penal justice.To achieve the goal of “making people feel fairness and justice in every judicial decision,” the Chinese Supreme People’s  Court continues to reform the standardization of sentencing.For this purpose,the Supreme People’s Court issued the “Sentencing Guidance for the People’s Court” (referred to as the Guidance) in 2008,which has since been revised six times.The Guidance provides comprehensive guidelines for the basic methods and steps of sentencing,the scope of common sentencing circumstances,and the sentencing of common crimes.However,real-world scenarios are complex,and the Guidance cannot cover all situations.Furthermore,differences in regional economic and social development levels,divergent rulings among individual judges,and the personal characteristics of defendants may lead to inconsistent sentences for similar cases.This may lead to a low rate of settlement,as seen in 2020 when the Supreme People’s Court heard a total of 1.12 million first-instance cases,of which about 11% and 2% went through a second trial and remand for retrial,respectively.This indicates that numerous cases have not yet been settled (without appeal or reverse appeal),and many of these may be controversial cases where judges may hold divergent opinions on specific sentencing such as imprisonment or fines.This low rate of settlement may also influence the judicial process and is detrimental to safeguarding the authority and credibility of the law.Some scholars have suggested that the individual judge’s discretion in sentencing should be compared with the collective experience of judges in sentencing.Judges who make rulings that reflect the collective experience should be supported and respected for their discretion,while judges who deviate significantly from the collective experience should have their decisions identified and corrected.Since 2016,the Chinese Supreme Court has vigorously promoted the construction of smart courts,hoping to use big data and artificial intelligence technology to discover judicial consensus.This would improve the accuracy and fairness of case acceptance and trials,and enable the judicial system to make fair and consistent rulings.

From the perspective of trial supervision,this article proposes a technical method for automatically detecting sentencing deviations.Since 2021,China Judgments Online (https://wenshu.court.gov.cn/) has released more than 100 million legal judgment documents to the public.This undertaking has provided a massive data foundation not only for research related to judicial judgments,but also for developing advanced machine learning algorithms that can automatically detect deviations in sentencing.In this article,we take the criminal judgment documents from 2018 as the sample and analyze a total of 460486 legal documents based on 62 charges.We then propose a method that can accurately detect abnormal situations of sentencing deviation in judicial trials.Specifically,the model includes the following three aspects.First,using the sentence as the dependent variable and the text extracted from the “as determined through trial” and “considered by the court” in the legal documents as the description of case facts,four deep learning models (LSTM,TextCNN,BiLSTM,Transformer) are constructed for sentence prediction.Second,based on the predicted results of the model,the difference between the predicted sentence and the actual sentence is calculated.Finally,we propose a heterogeneity index that is used to identify the charges that have deviations in sentencing.Inspired by the coefficient of variation in statistics,the heterogeneity index is constructed as

Heterogeneity index of the Rth crime =( Median of the absolute residual value of the R^th crime)/(Median of the sentence of the R^th crime)

In this article,the heterogeneity index is used to measure the degree of inconsistency between the model judgment and the judge’s judgment.Specifically,for the heterogeneity index of the Rth crime,the denominator “median sentence” represents an average sentence level for the crime in past judicial practices and can be roughly considered the average sentencing by judges.The numerator measures the average difference between the model judgment and the judge’s judgment for each case of the crime,which is very similar to the standard deviation in the construction of the coefficient of variation.Thus,the heterogeneity index can to some extent represent the degree of inconsistency between the model judgment and the judge’s judgment.In this context,if the prediction values given by the model and the judge’s ruling are basically consistent,it can be concluded that no dispute between human and machine judgments exists for that particular sentence.However,if the difference between the two is large,then inconsistency exists between human and machine judgments,and sentencing bias may be present.The calculation shows that the crimes of producing,copying,publishing,selling,spreading obscene materials for profit,crimes of harboring and sheltering,and crimes of misappropriation of funds are the top three charges with the highest heterogeneity index,which indicates that these three charges are most likely to result in cases of sentencing deviation.

To further quantify the factors that influence sentencing deviation and identify the judicial characteristics of such cases,this article takes the crime of misappropriation of funds as an example and conducts related empirical analysis.The results of regression analysis show that mitigating circumstances,such as confession,and statutory or discretionary sentencing factors have a mitigating effect on sentencing.The size of the misappropriated funds is directly proportional to the length of the sentence.However,there are also some phenomena that contradict judicial interpretations.For example,the defendant’s purpose for misappropriating funds has an impact on sentencing,which may be due to the prominent heterogeneity of this specific crime.The goal of this article is not to replace the judge’s ruling by accurately predicting the length of the sentence through establishing a model.Rather,it is to identify cases of sentencing deviation based on the established sentencing mechanism,provide reference and support for sentencing judicial practice,and enhance the normativity of the judicial system.

The number of registered firms is widely used as an indicator of economic vitality and entrepreneurship,ignoring the fact that registered firms may be unproductive and destructive.This paper is among the first to explore unproductive firms by analyzing the visit records of nearly 50000 randomly selected registered firms.Normal business activities require firms to be fully exposed to the market so that they can be contacted by their upstream,downstream,and peer parties.However,there are always firms engaged in unproductive activities that choose to hide their contact information and avoid exposure to the market.Due to the lack of data,little is known about unproductive firms,and even some basic facts are not clear,such as the percentage of unproductive firms and their causes.Using the number of registered firms as a measure of economic vitality or entrepreneurship can be biased if the difference between the number of registered firms and the number of firms engaged in productive activities is ignored.

The Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) 2023 sampled nearly 50000 registered firms from the China Business Registration Database.The field study shows that 35.2% of registered firms cannot be contacted by both address and telephone number.By matching the surveyed firms with other big data on economic activities,we find that the probability of an out-of-contact firm appearing in the blacklist of firms is 0.3% higher than that of contacted firms,and the proportion of those engaged in normal economic activities,such as posting online job vacancies,bidding for government projects,applying for trademarks and patents,is even lower.Finally,out-of-contact firms are more likely to be shell firms; their registered capital (in log form) is 14.1% higher than that of contacted firms,but their number of employees in social security (adding one and taking the log form) is 15.1% lower than that of others.Further analysis shows that out-of-contact firms are more likely to be in high-tech service industries with more industrial policy (as measured by the share of government consumption) and in regions with poorer business environments.

This paper contributes to the literature in two ways.First,this paper combines field study data with other big data to profile productive and unproductive registered firms in China.Since Baumol (1996) proposed a distinction between productive,unproductive,and destructive entrepreneurial activities,few studies have been conducted to analyze unproductive firms.Among the smaller body of literature,Desai et al.(2013) constructs a model of destructive firms; Sobel (2008) measures unproductive activities with the number of political and lobbying organizations in capital cities in the United States,and productive activities with patents; it finds that excellent institutional environments lead to a greater influx of entrepreneurial talent into productive activities. First, this paper enriches this strand of literature by offering stylized facts in the Chinese setting.Second,the findings of this paper could help scholars and officials to understand and properly utilize the registration database.Third,our finding indicates that industries with more policies tend to have a higher proportion of out-of-contact firms,highlighting the need for improved policy implementation.

Exchange rate,as the representation of the external price of a country’s currency,is a crucial part of the macroeconomic operation in China.In the macro field,exchange rate functions to connect the currency market,the foreign exchange market,and the macroeconomy,exerting significant influence on individuals’ and enterprises’ savings,credit,and investment behaviors.In the micro aspect,exchange rate serves as an important benchmark and target for asset pricing,derivative product design,risk management,arbitrage,and speculation.With the comprehensive opening of China’s financial market and the gradual progress of RMB’s free convertibility,the links among the currency market,the capital market,and the foreign exchange market are becoming closer and closer,resulting in a complex dynamic feedback system where exchange rate and other economic variables interact and influence each other.In the context of the current economic globalization,foreign exchange derivatives,as a common derivative instrument,are a comprehensive trading variety that combines investment,value preservation,and risk hedging functions.Compared with mature foreign derivative markets and their operational mechanisms,China is still in its infancy in this regard.For example,the current types of exchange rate financial derivatives in China are relatively limited and far from meeting market demand.Given this situation,it is imperative to develop China’s derivatives market,and the core of this task is to conduct in-depth research on the pricing principles of derivative products and implement them.Specifically,in line with the objectives of this paper,proper pricing of currency derivatives can not only hedge the profit risks of multinational enterprises,but also generate investment returns in the foreign exchange market,providing an important foundation and powerful tool for Chinese enterprises and institutions to participate in international financial market competition.Additionally,it can offer a scientifically rational reference for investors’ investment decisions and risk management,thus having significant practical implications in the flourishing development of China’s financial market.Therefore,this paper aims to closely connect with  cutting-edge theoretical research on exchange rate stochastic models internationally,with the goal of constructing excellent and practical currency derivative pricing models and methods.

The focus of exchange rate modeling lies In improving the empirical fitting effect of the model to the implied volatility surface,so as to better apply it to the development of China’s foreign exchange market.This paper aims to jointly model exchange rates and interest rates,derive analytical formulas for European option prices,and test their empirical fitting effect.In our model,the exchange rate and its volatility process are extended from the Heston model by introducing a jump term in the exchange rate process,while both domestic and foreign interest rate processes use the two-factor stochastic interest rate model (CIR).Our model incorporates three important empirical features:the correlation between exchange rates and their volatility,the presence of jumps in the exchange rate process,and stochastic interest rate processes.These features are beneficial for the development of complex exchange rate derivatives with longer maturities and help to expand the variety of Chin’s exchange rate derivative products.One important consideration is the examination of the correlation between exchange rates and their volatility with domestic and foreign interest rates.The model involves four processes,and if all of them are interconnected,the model becomes overly complex.Therefore,it is necessary to reasonably assume the correlation coefficient matrix.We assume that there is no correlation between domestic and foreign interest rates and exchange rates and their volatility.This modeling approach is selected based on empirical evidence,and it allows for the derivation of analytical formulas for various related derivative prices.These formulas can then be efficiently implemented using Fourier transform methods,thereby enabling efficient calibration of model parameters.The focus of this work is to ensure accuracy,efficiency,and stability in the implementation of the formulas,which can be tested by comparing the results with those generated by Monte Carlo simulations.

There is extensive discussion on what constitutes a good stochastic exchange rate model.Some argue that a successful exchange rate stochastic model should be able to generate appropriate implied volatility surfaces that are qualitatively consistent with market data.Others argue that a successful exchange rate stochastic model should be able to generate model prices for derivatives that are close to observed market prices.Often,models are selected through numerical optimization and goodness-of-fit or difference-related measures,as well as information criteria.This paper takes the latter view and uses a loss function constructed from exchange rate option prices,implied volatilities of exchange rate options,and zero-coupon bond yields to calibrate exchange rate model parameters via numerical optimization methods.

The findings of this paper have significant theoretical and practical implications.The four-factor model obtained satisfactory calibration results when using Monte Carlo simulation data for parameter calibration,demonstrating the effectiveness and robustness of the calibration method.Furthermore,when fitting real RMB/USD exchange rate option data,the model exhibited good performance.Additionally,besides exchange rate option data,the four-factor model also achieved a good fit with Chinese and US government bond data,which suggests its potential applicability in jointly modeling interest rates and exchange rates and pricing relevant derivatives,thereby contributing to the development of China’s foreign exchange market.

In contrast to traditional firms, platform firms expand primarily by extending market boarders to attract different types of users. This expansion strategy is affected by the platform’s market power and the affiliation status of their users. This paper studies the influence of platform’s expansion on its pricing strategy in the case of monopoly platform and oligopoly platform. We found that when the platform extends market boarders, the increment in the user scale by the cross-network effect will make the monopolistic platform tend to maintain the price of its original users.  Oligopoly platforms, whether the user is single-homed or multi-homed, can attract a larger scale of user group brought by the cross-network effect, although the price of platforms with extended market boarder is higher than that of platforms without extended market boarder.

In recent years,China's private equity market has undergone significant development and maturation.Compared with direct investment funds,Funds of Funds (FOFs) have developed rapidly by virtue of their qualities such as diversified investment risks,professional service management,and access to high-quality investment opportunities.Through empirical research,this paper analyzes the investment performance of private equity FOFs in China and explores the underlying factors that influence  the FOF performance.

This study analyzes performance variations and potential drivers of diverging performance across different types of private equity FOFs unique to the Chinese market.Our results suggest that FOFs underperform against direct investment funds.Limited partners with FOF background exhibit higher investment performance compared to those with a government background,but lower compared to those with enterprise,financial institution,and other backgrounds.Moreover,government-guided funds have significantly lower performance than market-oriented FOFs. Between the two market-oriented FOF types,state-owned FOFs have demonstrated superior performance in comparison to private FOFs.The performance of diversified FOFs is superior to FOFs with focused investments.Simulation sampling demonstrates that synthetic FOFs outperform real ones,indicating that the FOFs in the private equity market of China lack the ability to screen high-quality sub-funds.A preliminary investigation into the insufficient screening ability of FOFs suggests that the unsatisfactory screening ability of government-guided funds substantially diminishes the overall screening proficiency of FOFs.Despite considering the policy limitation of prioritizing local and early-stage investments,government-guided funds continue to exhibit inadequate screening capability.

The paper presents four primary contributions.Firstly,it conducts a pioneering and comprehensive examination of the investment performance of FOFs in China's private equity market from multiple perspectives.Moreover,it scrutinizes the factors behind the FOFs' performance and their capacity to evaluate sub-funds under various circumstances using various approaches.These analyses expand the frontier of academic research on the FOF performance.

Secondly,this study utilizes a novel calculation method to objectively evaluate the performance of Chinese FOFs based on the characteristics of the data available for Chinese private equity funds.International databases,such as Preqin,disclose the cash flow data of funds.Consequently,researchers can calculate the Internal Rate of Return (IRR) directly or utilize the Public Market Equivalent (PME) values provided by the database.However,cash flow data remains unavailable in China,and mainstream databases only offer IRR and investment multiples for each exit.Therefore,to calculate the investment performance of a direct investment fund,this paper employs the successfully exited projects' investment amount divided by the fund's total investment amount as a weight.The weighted average of the internal rate of return (investment multiple) for the successfully exited projects is then used to approximate the fund's internal rate of return (investment multiple).Meanwhile,this paper uses the successful exit ratio and IPO ratio of the fund's investment as measures of the fund's performance.Regarding FOFs,the investment performance is approximated by weighting the sub-fund performance (internal rate of return,investment multiple,successful exit ratio,and IPO ratio) by the ratio of a FOF's investment in the sub-fund to its total investment amount.None of the prior literature assesses the performance of China's FOFs or private equity direct investment funds using the aforementioned four metrics simultaneously.

Thirdly,based on Chinese characteristics,this paper innovatively studies the performance of FOFs at three levels,i.e.,it studies the performance differences between FOFs and direct investment funds,the performance differences between FOFs as LPs and LPs from other backgrounds,and the performance differences between FOFs from different backgrounds.In particular,the performance differences between the three camps of FOFs in the Chinese market,namely,government-guided funds,private FOFs,and state-owned FOFs,are studied for the first time.This paper enriches the literature on the performance differences of FOFs with different backgrounds.

Fourthly,this paper conducts random sampling using R to simulate the investment portfolio of the FOF as a way to determine whether the FOF can achieve better performance by selecting high-quality sub-funds.In particular,based on the local bias and the preference for early-stage funds in government-guided funds,we evaluate the ability of government-guided funds to select sub-funds through stochastic simulation.This is a dimension that has not yet been considered in the classic literature such as Harris et al.(2018).Our paper pushes forward the frontier of research in this regard.

In addition to the theoretical value,this paper also holds practical significance.The performance difference analysis in this paper can provide guidance for various types of China's FOFs,especially government-guided funds,to further reduce costs,improve investment efficiency,and enhance investment skills.For investors,the analysis in this paper can help them choose the right type of FOF and management style,and enhance the return on capital.For fund managers,it can facilitate them to make peer comparisons,summarize lessons learned in a timely manner,and enhance the efficiency in competition among different funds.Additionally,the methodology presented in this paper can aid supervisory authorities to scientifically evaluate fund performance,leading to more effective supervision and increased efficiency in the private equity market's support of the real economy,entrepreneurship,and innovation.

To survive fierce competition,firms can invest in product innovation and cater their designs to match the preferences of certain consumer segments,thus cultivating brand loyalty.However,it is well-documented in the marketing literature that firms sometimes would rather direct their efforts in making consumers believe (or misbelieve) that the products are more differentiated than they actually are,so that a loyalty premium can be commanded even when true product values are very similar.In particular,firms often obfuscate product information through complex offerings,confusing pricing,excessive features,and limited disclosure,making it difficult for consumers to make informed decisions.This phenomenon appears to contradict the conventional,neoclassical framework,which posits that consumers make optimal decisions based on perfect rationality and that firms compete on quality and price rather than by engendering confusion.

How can we explain the pervasiveness and persistence of obfuscation in real market competition? The emerging literature on behavioral industrial organization adopts a novel perspective premised upon consumers’ bounded rationality and cognitive biases.In this article,we focus specifically on one such bias—correlation neglect,which refers to the tendency to underestimate or even completely neglect the correlations between various information sources,and which has attracted significant research interest recently.In this paper,we provide a theoretical framework to explore how this type of consumer naivete impacts firms’ competition strategies and overall social welfare.

To illustrate the key idea of our paper,consider two fund management companies offering investment products based on very similar underlying assets.Although aware that their products’ returns are highly correlated,the companies may prefer not to convey this fact to consumers,because doing so would intensify fee competition.Instead,the companies may engage in obfuscation,with the aim of generating the perception that their offerings differ significantly.For instance,they could use distinctive industry jargon to describe their investment portfolios,cherry-pick performance benchmarks for comparison,or highlight either the impressive credentials or dazzling prior performance of their fund managers.Were consumers rational enough and able to discern the underlying homogeneity of the two products,such informational obfuscation would prove ineffective.However,if consumers evaluate each piece of information that they receive in isolation without properly accounting for the prior correlation,the companies’ obfuscation tactics could succeed in creating an illusion of differentiation.In this way,correlation neglect grants the companies exaggerated market power to charge higher fees than competition would otherwise necessitate.

The key premise in the example above is that even if aware of the potential correlation between competing products,consumers may fail to assess it accurately,let alone fully incorporate it into their decisions.Formally,correlation neglect refers to the cognitive bias where individuals underestimate or even completely ignore the correlation between different information sources when updating beliefs on which choices are based.To study the implications of such biases for market competition,this paper develops a duopoly model with correlation-neglecting consumers.We demonstrate that equilibrium outcomes differ substantially across settings.With perfect knowledge and accurate accounting of correlation,firms have no incentive to obfuscate because competition eliminates any potential gains.However,when consumers underestimate or neglect correlation,firms can often obfuscate to soften competition and earn extra profits at the expense of consumers.Our results offer a rationale for firms using misleading marketing messages,while shedding light on how policy interventions such as consumer education or mandatory basic goods may help or backfire.In sum,the core contribution of our analysis is to show how correlation neglect enables obfuscated marketing to emerge and persist,which could have implications for future research on other biases or market structures as well.

In the paper,we present a two-stage duopoly competition model in which two firms compete on marketing and price for customers.The valuations may vary between products,but can be arbitrarily correlated.Here,the true degree of correlation between product values can be interpreted as a measure of product differentiation.Consumers cannot directly observe the true valuations,but instead receive a signal from each firm,which is composed of the true valuation of the product offered by that firm and an unbiased noise with variance being chosen strategically by the firm.The noise that one particular firm adds to the signal may change consumers’ valuation for its product,but does not affect consumers’ willingness to pay for the competing product.In the first stage,two firms simultaneously choose their obfuscation strategy.Upon receiving signals from both firms,consumers update their beliefs about product values.In the second stage,the firms post price and engage in the conventional Bertrand competition.Essentially,we assume that firms can manipulate perceived correlation and valuations by providing noisy signals about product values,thereby impeding product comparison.

We first establish a benchmark result: if consumers are rational enough to properly assess and accounting for the true correlation, firms would opt for maximal transparency of product information. Then we turn to our main discussion of the impact of correlation neglect.Here,consumers understand the information provided by each firm in isolation, but they hold incorrect beliefs about the correlation between the true product values and, as a consequence, misjudge the interdependence of the signals that they received. We show that when products become sufficiently homogeneous, firms would adopt a moderate level of obfuscation for their marketing strategies. The equilibrium results and subsequent comparative statics and welfare analysis show that as the gap between the true and perceived degrees of correlation increases (i.e., as consumer naivete increases),firms’ profits rise while consumer/social surplus decreases due to a higher probability of mismatch in purchase.

Lastly, we explore two extensions of our main analysis.First,we characterize the conditions under which asymmetric equilibria can also emerge in our setting. Comparing the symmetric and asymmetric equilibria suggests that the asymmetric equilibrium yields higher profits for firms but a lower surplus for consumers.Second,we extend the discussion to general value distributions, demonstrating the robustness of our core insights.

Digitalization is a critical factor in the modernization of traditional industries,but there is still a lack of quantitative assessment of the current level of digitalization in China's universal enterprises in academia.Specifically,it is unclear whether Small and Medium Enterprises (SMEs),which account for 90% of registered firms and 80% of urban employees,have adopted any digital technologies.The paper titled ‘Stylized Facts about Digitalization Processes of Chinese Enterprises—Evidence from the Enterprise Survey for Innovation and Entrepreneurship in China' aims to answer the question of which digital technologies Chinese enterprises adopt and how these technologies impact their business.It also aims to explore why some firms choose to conduct digital transformation while others do not.The study is based on the Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) conducted by the Peking University's Center for Enterprise Research.The Center for Enterprise Research designed the ESIEC 2023 questionnaire to collect information on the use of various digital technologies by sample enterprises.The questionnaire was informed by academic literature and surveys conducted in Europe and the United States.The collected results can be categorized into the following four types of digital technologies:① The term ‘Internet’ refers to various online platforms such as e-commerce,self-owned websites,and apps that are primarily used for external operations.② ‘Digital management’pertains to electronic data storage and professional business software that are primarily used for internal management. ③ The‘Online business division’ includes cloud services and internet outsourcing.④ ‘Digital machines’refer to CNC machine tools and industrial robots that are primarily used in manufacturing enterprises.ESIEC 2023 collects micro-survey data at the enterprise level through stratified sampling and interviewer field surveys.The sample includes all registered companies and individual households in the past six years in six provinces and cities:Liaoning,Shanghai,Zhejiang,Henan,Guangdong,and Gansu.

In this paper,we first estimate the digitalization process of new private enterprises of different industries and sizes by calculating the enterprise adoption ratios and employee penetration of four digital technologies.We summarize the stylized facts of the current digitalization process of Chinese enterprises from multiple perspectives,by comparing the differences in terms of enterprise type,regional distribution,and industry distribution.We comprehensively examine the level of digital transformation of Chinese enterprises through international comparisons using surveys conducted in Europe and the United States.Additionally,we explore the factors that influence enterprise digital transformation.

The paper's findings reveal that the digitalization of Chinese enterprises is rapidly developing and has a profound impact on the labor market.Specifically,61% of Chinese firms have adopted at least one of the digital technologies in Internet use,digital management,Internet outsourcing,and digital machines,affecting 85% of the employed population.However,there is significant heterogeneity among the different digital technologies.Among corporate companies,approximately 64% have adopted electronic data preservation.The adoption rate of digital management software,Internet platform operation and e-commerce sales is about 35%,and the adoption rate of cloud services,self-hosted websites and applications,and industrial robots is about 10%.Third,there are regional differences in the digitization process of enterprises.Zhejiang Province has the highest digital technology penetration among the six provinces,affecting 93% of the employed population,while Liaoning and Henan Province has the lowest digital technology penetration among the six provinces,but still reaches over 80%.Fourth,there are differences in the digitalization process of enterprises across industries.Business services have the highest digital technology penetration,affecting 91% of the employed population,followed by industry at 86%,residential services,and agriculture with the lowest.Fifth,in general,Chinese enterprises are still lagging behind European and American countries in terms of digital transformation.Chinese enterprises have a lower adoption ratio (39%) in Internet platform operation compared to European enterprises (59%).However,they have a developmental advantage in the adoption of e-commerce and short-video platforms.Additionally,the adoption ratio of Chinese enterprises in digital management is only half of that of U.S.and European enterprises.Sixth,this paper highlights that enterprise digital transformation is influenced by sizes,ages,and wage.Specifically,companies that have larger sizes,smaller ages,and higher wages are more likely to implement digital transformation.

This paper has the following outstanding features:First,objectivity,this paper's metrics for enterprise digital transformation are based on specific digital technology adoption in micro-survey data.The constructed indicators objectively reflect the real situation of enterprise digitization.Second,multidimensionality,this paper utilizes research questionnaires from Europe and the United States to refine four specific technologies:Internet use,digital management,online business division of labor,and digital machines and other digital technologies.Third,representativeness,this paper uses ESIEC 2023 data sampled based on the business registration database of enterprises,and the research object covers enterprises of different industries and sizes.Fourth,timeliness,this paper analyzes the digital transformation of the newly built enterprises in the last six years in 2022,which can reflect the latest situation of the digitalization of China's industries.

China’s coal-based energy resources endowment and the current stage of its economic and social development determine that the economic and social development will remain inseparable from coal for a considerable period of time in the future.Even with the “dual carbon” target,coal still  plays its role as a basic energy source,and provides energy support for economic and social development.

The coal mining industry is recognized as a high-risk industry where human behavioral factors are the direct cause of the vast majority of accidents.The monitoring system enables timely detection of unsafe miner behavior and timely intervention or change of unsafe miner behavior,which can effectively prevent accidents from occurring.However,safety and security in coal mines can be compromised by the responsibility,work fatigue and efficiency of safety monitoring staff.Therefore,it is important to study automation methods based on machine learning to replace manual monitoring using artificial intelligence technology in order to achieve automatic identification of unsafe  behavior in underground mines in a safe and efficient manner over a long period of time to ensure coal mine safety.

Specifically,it is hoped that the high-frequency,high-resolution image data captured by surveillance cameras will be used as input to develop appropriate statistical and deep learning models for the timely detection and correction of unsafe behaviors in mine production operations.Taking the identification of illegal behaviors by miners on monkey vehicles as an example,the essence of the problem is target detection and target identification.The current mainstream algorithms can be divided into two-stage methods and one-stage methods.Two-stage methods,such as R-CNN and Faster R-CNN series algorithms based on candidate regions.One-stage methods,such as Yolo and SSD algorithms,which are currently receiving great attention.Although these methods have played an important role in target detection,they are difficult to apply to the mine safety management issues we are concerned about,mainly for two reasons.Firstly,annotation is costly.Specifically,there are many types of roadways in coal mines and complex working faces,so it is very expensive and time-consuming to do bounding box annotation for the massive amount of data collected by each surveillance camera.Secondly,the method requires a large sample size.Although a large number of images can be extracted from mine roadway monitoring video,there are fewer images with miners present.This kind of image can be extracted mainly during rush hours.It is also worth mentioning that these images have extremely high similarity and cannot provide too much variability for model training.At the same time,due to the strong implementation of safety management,the vast majority of miners have not engaged in unsafe behavior,so relevant images constitute a large number of negative examples and a very low proportion of positive examples (images with unsafe behaviors).There is therefore an urgent need for a new methodology,applicable to smaller sample sizes.

For these reasons,we will mainly face two challenges:Firstly,how to quickly identify key pixel regions containing miners without the support of annotation boxes.Secondly,based on the discovered key areas,how to construct a high-precision deep learning model with the support of small samples.With regard to the first challenge,we use for reference to the background subtraction algorithm to obtain the target region by performing a subtraction operation between the current frame in the image sequence and the background image.The characteristic of this algorithm is its fast calculation speed,but its disadvantage is that the calculation results are unstable.To solve the stability problem,we propose a three-stage solution.In the first stage in outlier discovery,the median method is used to estimate the standard deviation instead of the traditional moment estimation,so as to obtain a more robust outlier region.In the second stage,the outlier area is smoothed on the pixel plane to obtain more stable results.In the third stage,the smoothed outlier region,bounded by a certain threshold,is adaptively extracted for important pixels and then the classical connected domain algorithm is used to do the integration in order to obtain the complete local region and thus obtain a high-quality effective miner sub-image dataset.With regard to the second challenge,we adopt the transfer learning method to leverage the pre trained results on the classic big data set to minimize the requirements on our sample size.Specifically,for the obtained miner sub-images,category annotation is directly performed to avoid complex bounding box annotation,saving expensive manual annotation costs.Transfer learning is adopted,and Google’s open source MobileNet model is selected as the framework of transfer learning.

The experimental results show that,compared with the traditional estimation,the thresholds constructed based on standard deviation obtained from the median estimation make the miner sub-images extraction accurate and robust.Finally,the authors use the MobileNet model for transfer learning,and the results further show that the miner sub-image dataset obtained by the median estimation can provide higher precision than that of the traditional estimation.The resulting out-of-sample prediction accuracy and AUC are excellent in classifying the miners’ actions into safe or unsafe category.

Finally,some feasible research directions for future work are proposed.Firstly,the images in the video frames are highly similar over a period of time in close proximity.For convenience,this article extracts images at equal intervals,which does not fully utilize temporal similarity.In the future,further research will be conducted to improve the efficiency of sub-images extraction by utilizing temporal similarity.Secondly,further in-depth research should be conducted on a series of safety behavior identification problems in mine roadways such as environmental monitoring and equipment operation,in order to improve the intelligent analysis technology and level of mining safety monitoring.

Innovation is a key driver of productivity,enhancing enterprise competitiveness,performance,and national economic growth.Therefore,technological upgrading and innovation have become crucial for national development and transformation.

For Micro,Small,and Medium Enterprises (MSMEs),innovation holds increasing importance for survival and development.However,the research on the innovation activities of MSMEs remains more limited compared to larger firms in large due to lack of data.

The Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) addresses this data gap by conducting surveys specifically on MSMEs.This article first reviews the theories and literature related to innovation measurement and innovation surveys.It then introduces the ESIEC questionnaire design,analyzes the basic facts of enterprise innovation data in ESIEC surveys,and finally employs the CDM model to analyze the relationship between R & D,innovation,and productivity in MSMEs included in the ESIEC surveys.The results confirm consistency with existing literature on the relationship between these three factors.Compared to existing micro-level innovation data in China,the ESIEC survey offers four key advantages:①It covers multidimensional innovation measurements and comprehensive information on innovation,including not only standard international innovation survey data but also incorporating indicators relevant to China's unique economic characteristics and high-quality development goals; ② It includes small and micro-enterprises as survey subjects; ③ It encompasses a variety of service industries alongside the manufacturing sector; ④ Unlike many online or telephone surveys,ESIEC utilizes face-to-face interviews conducted by rigorously trained interviewers with enterprise leaders or executives,ensuring data quality.In conclusion,ESIEC data provides a valuable data source for analyzing innovation activities of MSMEs in China.Preliminary analysis based on ESIEC data aligns with the existing literature,suggesting reliable data and the possibility of comparative analysis with well-known surveys,such as  the  Community Innovation Survey (CIS) and the Annual Business Survey (ABS) in the United States.

As the e-commerce market matures,competition among e-commerce platforms has become increasingly intense.The difficulty of attracting new customers is far greater than maintaining existing ones,making customer repurchases a crucial means for e-commerce platforms to increase profits.Predicting customers’repurchase tendencies/frequencies is key to formulating marketing strategies for these platforms,attracting widespread attention in fields such as marketing,operations research,statistics,and computer science.Predicting customer repurchase tendencies also helps marketers understand the main factors affecting consumer loyalty,thereby better serving platform customer relationship management.Existing research often relies on theories of consumer behavior,proposing hypotheses and using methods like surveys and structural equation modeling to confirm factors influencing consumer repurchases.Some studies adopt data-driven approaches,using models like random forests to predict consumers’repurchase intentions.As e-commerce accumulates more data,data-driven research methods are gaining importance.However,these methods are limited to modeling frequency domain indicators and struggle to depict consumers’online browsing trajectories.Consumers’online shopping behaviors not only record their product-seeking process but also reflect their shopping intentions,which can,to some extent,indicate their repurchase intentions.Common approaches transform online shopping behaviors into frequency domain indicators like click counts for modeling,which fails to effectively depict the popularity of clicked products on e-commerce platforms and also obscures the interaction between consumers and products.Complex network analysis methods offer new insights into mining online consumer behaviors and have been applied to some extent.Studies show that the number of links to a product associats with its demand,and the centralization of similar product networks impacts the demand for focal products.Therefore,using complex networks to depict consumers’online clicking behaviors and extracting relevant features can significantly improve the accuracy of  repurchases prediction.Beyond accuracy,marketing is more concerned with model interpretability.An interpretable prediction model can help us grasp the factors affecting consumer repurchase intentions,thereby avoiding risks due to unmet marketing expectations.

This study proposes an interpretable consumer repurchase prediction model based on clickstream networks.The model,grounded in consumer behavior theory,employs complex network methods to measure users’browsing activities and extracts features that characterize product popularity and consumer behavior,ensuring a degree of interpretability of the extracted features.It then uses classic machine learning models to predict whether a consumer will repurchase the same product within 7 days.Through a series of comparative experiments,the study demonstrates that the three sets of features extracted based on consumer behavior theory—product click features,consumer click features,and interaction click features—all enhance the accuracy of repurchase predictions.Moreover,the removal of any one category of features from the feature set constructed from the clickstream network significantly decreases prediction accuracy compared to the model with complete features,further confirming the necessity of including clickstream features in the prediction model.In terms of the model’s interpretability,the features extracted on the basis of consumer behavior theory in this study have inherent interpretability.This is further confirmed by post-hoc analysis using Shapley values,which also validate the importance of the extracted features.Finally,robustness analysis,including Lasso feature selection and adjusting the proportion of training samples,also proves that the method proposed in this study has a stable effect.Therefore,the interpretable consumer repurchase prediction model based on clickstream networks proposed in this study shows relatively good performance in terms of prediction accuracy,interpretability,and robustness.

This research interprets the role of clickstream networks in predicting repurchase intentions from a big data-driven perspective.Compared with classic theory-driven studies,this research may not reveal the causal relationship between clicks and repurchase intentions,but by modeling repurchase intentions,it can provide references and insights for business operations management.We believe that in the process of making recommendations,businesses should,on the one hand,recommend products with a high likelihood of repurchase to consumers; on the other hand,they should reduce recommendations of products with particularly low purchase intentions to consumers.To enhance consumer purchase intentions,it is necessary to combine theory-driven approaches for argumentation,which is where data-driven methods fall short.In terms of research methodology,although the features extracted in this paper based on theories such as consumer behavior have a certain degree of accuracy,robustness,and interpretability,they are still limited compared to the automatic feature extraction of deep learning methods.Regarding the research data,the method in this paper only uses one month’s data,and both the indicators and data have certain limitations.However,as a data-driven research method,it holds practical significance.In future research,we will explore the use of more advanced methods for modeling,such as deep graph neural networks,and further propose more management-relevant research questions based on business practice,develop more data,and test these in the application process within businesses.

改革开放已经四十多年，世界迎来了百年未有之大变局。在全面推进中国式现代化的征程中，为了学习和传承厉以宁先生的思想和精神，北京大学光华管理学院于2023年11月26日举办了厉以宁先生追思会暨厉以宁学术思想研讨会。会议邀请了厉以宁先生生前各界的挚友、同事、学生，他们是中国改革开放的见证者和实践者，也是中国经济管理学科领域的杰出代表。大家围绕着厉先生的学术思想、治学精神和为师之道展开了深入讨论，共同追思弥足珍贵的历史岁月，一起讲述一位思想大师的学术人生。《经济管理学刊》编辑部现将与会者的精彩发言整理成文，以飨读者。

With the rapid development of financial technology (i.e.,FinTech) today,credit risk has always been the focus of the financial industry.When analyzing individual users’ credit behavior or assessing their credit risks,existing studies mainly focus on some basic or static information about users.Combined with the increasing popularity of digital payment transactions in recent years,this paper uncovers the relationship between consumer behavior and credit default.To understand the relationship between consumption and credit behavior,inspired by the self-regulation theory,we focus on two product categories:health and education.Self-regulation theory suggests that people engage in deviant behaviors due to self-regulation failure.Such failure is rooted in exposure to excessive impulses,as the energy resources in the human body can inhibit only a limited number of impulses.Consequently,failure to control oneself regarding one thing is more likely to be accompanied by self-regulation failure respecting another thing.A financial credit deviation is a form of self-regulation failure,as it runs counter to one’s fulfillment of financial obligations and is always caused by overspending and limited savings.Conversely,consumption in health (especially physical exercise and health products) and education (especially books and self-development) may indicate successful self-regulation,because people need to effectively manage and control motivations and behaviors to plan for learning or exercise,in order to improve themselves in positive ways.Therefore,we postulate that the consumption tendencies within these two categories are associated with people’s credit behavior,which postulates,of course,requires empirical validation.

Empirically speaking,we collaborated with Tencent.Recently,in 2020,Tencent released a new consumer loan product,called Fenfu,which allows users to spend money first and then apply for a loan later.With such a loan product,users can enjoy,in effect,a post-payment shopping experience.For those who choose to adopt Fenfu,they are first assigned with a credit line,which is set by Fenfu’s experts based on demographics.Users can repay at any time after consumption and Fenfu charges its users a fixed daily interest rate according to the daily unpaid amount.For the purpose of this research,we analyze 22000 Fenfu users randomly selected from its nationwide pool.Fenfu assigned a label for each user to indicate whether he/she had ever defaulted up to the time that we collected data,in June 2021.We next obtain granular information on each user’s historical transaction records that had occurred within the 6 months prior to their Funfu activation dates.We obtain 4187445 records in total.We adopt a standard econometric approach combining propensity score matching and logit model estimations to verify the relationships between consumption tendency and credit risk.Our empirical analyses provide strong evidence supporting our theoretical justifications:that is,users with a higher consumption tendency toward health-and education-related products present a lower default probability.

Statistically speaking,we observe that the default probability decreased by 3.2% for health-type users as compared with the control group.Meanwhile,the probability of default decreased by 2.0% for education-type users as compared with the control group.Furthermore,we analyze the impact of future orientation on user default.The results show that users who value future-oriented consumption of related products are less likely to default.Our empirical results show that future-oriented consumption in the health or education category had significant negative effects on credit default behavior.Interestingly,we notice that the effect sizes were larger than the average consumption pattern in the health or education category,indicating that future-oriented consumption tendency could better reflect an individual’s self-regulatory behavior,thus resulting in a lower default probability.

Our work contributes to the existing literature in multiple ways.In particular,we expand the scope of the application of self-regulation theory to the analysis of individuals’ financial behavior.Based on this stream of literature,we disentangle both theoretically and empirically the relationships between an individual user’s consumption tendency and financial credit risks.That is,we bridge the gap between the two types of individual behavior,which,in turn,provides the theoretical foundations and practical guidance for the usage of consumption records in the FinTech industry.The aforementioned theory-guided feature construction enables us to build sound relationships between the constructed consumption features and individual credit behavior,which boosts our understanding of the underlying psychological mechanisms of financial deviation.Whereas domain theories are applied to guide the choices of possible features and relational hypotheses to explain outcomes,it would be even more intriguing if the theory-driven features can be employed in a prediction model to achieve satisfactory prediction performance,which by implication,has the essential advantage of both generalizability and explainability.Regarding managerial implications,we unravel the relationships between consumption and credit behavior.In particular,we identify three product categories and proved their value,both theoretically and empirically,to any examination of a target user’s credit risk.Financial professionals will be able to leverage our empirical findings in order to augment their decision-making; they will also be eager to scrutinize the product categories highlighted by our study for a deeper understanding of consumer behavioral tendencies.

Scientific researchers are an important force in promoting the development of disciplines.In the era of big science,more and more scholars tend to cooperate in research,and the phenomenon of scientific research cooperation is becoming more and more common.Through scientific research collaboration,scholars can complement each other’s strengths and avoid duplication of research.The collaboration among researchers can be transformed into network data.The application of complex network analysis has led to significant advancements in understanding complex systems across various fields.Therefore,in order to understand the current mainstream research topics and the collaboration mode among researchers,this study collects information of 66460 papers published in 44 statistical journals from 2001 to 2018,and builds a co-authorship network.It can be found that in recent years,statisticians have increasingly tended to collaborate in publishing papers.Besides,nearly one-fifth of statisticians have only one collaborator in the network.Professor Balakrishnan,N.,from McMaster University,has the largest number of collaborators and has published the most papers in 44 statistical journals.Many large networks have a core-periphery structure,and so does the collaborator network.We then extract its core network.The core network contains 1158 nodes and 15464 edges.Additionally,there are 34 connected components in the core network.The largest connected component has 356 authors,accounting for 30.7% of the total authors in the core network.The second largest connected component has 172 authors,accounting for 14.9% of the total authors in the core network.The rest of the connected components have less than 100 authors,respectively.Then we particularly analyze its first and second largest connected components.

There are three common characteristics of complex networks,which are small-world,scale-free,and community structure characteristics.Among them,the community structure means that nodes in the network show aggregation phenomenon.Community detection is a particularly crucial research area,as it allows for the identification of groups of nodes that exhibit specific patterns of interaction within a network.We intend to conduct community detection in the first and second largest connected components.For many community detection algorithms,the number of communities should be pre-set.However,it is difficult to know it in a real network.Using cross-validation to automatically select the number of communities in the network is a breakthrough in the field of community detection.This paper adopts the edge cross-validation (ECV) method to determine the number of communities and adjustment parameters.Then we use the regularized spectral clustering algorithm to discover the community of the co-authorship network.

The core co-authorship network is divided into 62 communities.Through observation,it can be found that authors in the same community cooperate relatively closely,and authors belonging to different communities have relatively little cooperation.There is cross-collaboration between different communities.Then we analyze the characteristics of 62 communities from the three perspectives based on the authors’ attributes.The first one is the research field.We find 29 different research fields,including biostatistics,variable selection,maximum likelihood,and many others.We specifically show the research fields of the communities in the largest connected component.The authors in the largest connected component have a broad range of research topics.Some communities focus on more than one field.The second one is the journal.There exists an obvious similarity in the journals in which authors from the same community published their papers.We take Community 6 and Community 18 as examples for detailed analysis.It is found that authors in Community 6,who mainly study survival analysis in biostatistics,are more inclined to publish papers in Bioinformatics,Biostatistics,Biometrics,and other biostatistics journals.Authors in Community 18,who mainly focus on variable selection,are more inclined to publish papers in the top statistical journals such as Journal of the American Statistical Association,Biometrics,and Annals of Statistics.The third one is the author’s affiliation.The affiliations of authors in the same community are also obviously clustered.We also take Community 6 and Community 18 as examples for detailed analysis.It is found that many of the statisticians in Community 6 are from the University of North Carolina Chapel Hill and the Fred Hutchinson Cancer Center.Community 18 has a higher number of statisticians from the University of North Carolina and the University of North Carolina Chapel Hill.

Most of the existing studies about co-authorship networks of statisticians focus on papers in the four major statistical journals (Annals of Statistics,Biometrika,Journal of the American Statistical Association,and Journal of the Royal Statistical Society Series B-Statistical Methodology).Comparatively,the data used in this paper covers a much broader range,and the conclusions are richer.The methods used to determine the number of communities in previous studies are more subjective.Compared with them,the method adopted in this paper is more objective and universal,which can be extended to collaboration networks in other fields.

Under the current governance reform goal of innovation-driven high-quality development in China,mobilizing institutional investor shareholders to actively participate in corporate governance is an important incentive mechanism.However,due to the “strong speaking right of controlling shareholders and weak speaking right of institutional investors” in  the capital market,the participation of institutional investors in corporate governance in China still confronts many restrictions.For institutional investors with low shareholding,their institutional ability in information collection,data analysis,and governance organization brings little feedback and effect.However,institutional investors can choose to jointly cooperate in corporate governance,which may alleviate the problem of insufficient shareholder rights of individual investors and strengthen the total governance effect.Therefore,this paper aims to analyze the behavioral mechanism and economic consequences of how institutional investors cooperate to strengthen corporate governance effect theoretically and empirically.

In theoretical research,we construct a sequential game model equilibrium to describe the cooperative behavior of mutual funds.Individual fund needs to select a limited number of adjacent funds to conduct governance cooperation under the constraint of behavioral energy ceiling,and maximize the corresponding governance benefits on her own portfolio.The equilibrium result of the cooperative game shows that the cooperation decisions among individual funds will form in the distribution characteristics as different net communities.The funds in the same network community will have high-quality cooperation relationship in governance,and can effectively promote corporate innovation.

In empirical research,we define the information associations between paired funds in the same network community as cooperative relationships,and define the information associations between funds in different net communities as non-cooperative relationships.Using the innovation data of Chinese listed companies and the fund holding structure data from 2007 to 2021,this paper obtains the following empirical results.First,this paper finds that the intensity of cooperative relationships for shareholding funds is significantly positively correlated with the level of future corporate innovation activities,indicating that there is a governance cooperation effect among individual institutional investors and it has an important incentive effect on corporate innovation.Second,further research on the cooperation behavior among funds finds that there exist two types of specific cooperation channels,namely “Joint Site-visiting” and “Synchronous Trading”.“Joint Site-visiting” refers that two funds with community cooperation relationship have a higher probability of jointly participating in site-visiting on target listed company on the same day than funds without cooperation relationship.“Synchronous Trading” refers that two funds with community cooperation relationship that tend to trade securities in a similar direction and proportion.Third,further examination on cooperation effect of funds in the same network community structure shows that the governance incentive of cooperative funds is affected by the governance willingness of institutional investors.Funds with stronger cooperation willingness can urge both parties to participate in “Joint Site-visiting” more frequently,and strengthen the incentive effect on the innovation activities of listed firms.

The conclusion of this paper has the following contributions.First,this paper deepens the understanding of institutional investor information network structure.This paper finds that the competition and cooperation of rational institutional investors for information resources is the internal economic reason for the formation of information network structure.In the net community structure of institutional investors proposed in this paper,the information competition behavior of institutional investors for governance cooperation chances is significantly different from the characteristics of information-sharing behavior.For example,the relationships on network community cooperation are exclusive,and need to be agreed by both parties,and cannot be transferred to third parties.Second,this paper extends the mechanism theory of institutional investors' participation in corporate governance.Based on the reality that individual institutional investors in  the Chinese capital market have weak speaking rights,this paper further studies the cooperation mechanism of institutional investors in corporate governance.We find that the cooperative governance behavior of institutional investors under the structure of network community can significantly strengthen the governance effect on the corporate,which can enhance the governance willingness of institutional investors and provide a new research perspective on corporate governance cooperation.

The findings of this paper have several important implications for market regulators,institutional investors and listed companies.First,for market regulators,this paper shows that encouraging cooperative behavior of institutional investors can alleviate the imbalance state of governance right in the market and help stimulate the modernization of China's market governance ability.Meanwhile,comprehensively cultivating and developing institutional investors with different investment styles in the market can stimulate more cooperative governance on listed firms.Second,for institutional investors,this paper shows that asset management managers need to overcome the negative thinking of “free riding”,and shift from passive shareholding to active supervision,playing an effective role in corporate governance through cooperative behavior.Third,for listed corporate,they can provide more convenient cooperation environment and communication platform for institutional investors,which can help in reforming the existing governance environment and effectively supervising the behavior of controlling shareholders.

Product appearance is one of the key attributes that determines a product’s market success.Previous studies have mainly focused on the dimension of aesthetics and found its positive effect on product evaluation.This effect occurs partially because a more aesthetically pleasing design indicates more efforts the firm has devoted to the product,which is appreciated by consumers and thus increases their product evaluations.Our research extends this line of work by examining the dimension of complexity in utilitarian product appearance design.We propose that although a more complex design also indicates more efforts,it may decrease product evaluation by inducing consumer inferences that the firm has devoted more to product appearance and thus less to the product’s key attributes.These inferences reduce the perceived quality of the product and consequently product preference.

We tested our hypotheses with six studies.Study 1 comprised two experiments testing the main effect of design complexity on product preference.In both studies,a single factor (product design:simple vs.complex) between-subjects design was used,and participants were randomly assigned to one of the two conditions.Participants were shown an image of a simple or a complex design product and asked to indicate their preferences for the product.In addition,the participants were asked to rate the quality of the product.In line with our expectations,the complex-designed product received a lower preference rating than the simple-designed product.Moreover,the main effect was mediated by participants’ perceptions of product quality,such that when they viewed the complex-designed (vs.simple-designed) product,they inferred the product’s quality to be inferior,and thereby lowering their preferences for the product.Study 2 adopted the same experimental design as study 1,and used USB flash drives as stimulus for generalizability.After participants reported their preferences for the complex-designed versus the simple-designed product,we also measured their perceptions of product quality and their inferences about the firm’s effort in the product’s key attributes.The results replicated the finding of study 1,such that participants preferred the complex-designed USB flash drive less than the simple-designed one.In addition,we found that this effect was sequentially mediated by inferences about the firm’s efforts (mediator 1) and perceptions of product quality (mediator 2).Study 3 used two separate experiments to test the moderating role of consumer mindset (i.e.,zero-sum mindset) in the proposed effect.Specifically,study 3A adopted a 2 (product design:simple vs.complex)×2 (mindset:zero-sum vs.non-zero-sum) between-subjects design,and participants were randomly assigned to one of the four conditions.We first presented participants with a USB flash drive that had either a simple or complex appearance design.Then,based on prior research,we manipulated participants’ mindsets by telling them that a firm’s resources allocated to product appearance design and key attributes were non-competitive (i.e.,non-zero-sum condition) or competitive (i.e.,zero-sum condition).After that,participants evaluated the quality of the USB drive.The results indicated a significant interaction between appearance design and consumer mindset on product quality perception.As predicted,participants primed to hold a zero-sum mindset perceived the complex-designed USB as lower in product quality than those with a simple complex appearance design.However,this effect diminished when participants were primed to hold a non-zero-sum mindset.In study 3B,we manipulated consumer mindset as a between-subjects factor and asked participants to make a choice between a simple-and a complex-designed product,followed by a measure of their inferences about the firm’s resource allocation.The results supported the moderating effect of consumer mindset,so that participants holding a stronger zero-sum belief inferred that the firm had allocated fewer resources to key attributes of the product with a complex design and thus preferred the product less.Study 4 tested the moderating role of firm size.This study used a single factor (firm size:small vs.large) between-subjects design.We manipulated firm size by informing participants that the product was made by a small or a large company.Then participants were presented with a complex-designed and a simple-designed product,and asked to indicate their preferences.We found a significant impact of firm size such that the higher preference for the simple-designed over the complex-designed product decreased with the firm size.

This work contributes to the research streams on product design,zero-sum mindset,and cue utilization theory.In addition,our findings provide actionable suggestions for companies on when to adopt simple appearance designs to promote their products. 

The COVID-19 has caused a huge negative impact on China's economic development and stable social operation,and the harm caused by this kind of infectious disease and its governance have become the focus of attention of all sectors of society. In fact,infectious diseases have always been a major risk and hidden danger threatening the lives and health of our people. According to the China Health Statistics Yearbook,the average incidence rate of infectious diseases in China in 2019 was as high as 73357/100000. 

Therefore,strengthening and improving the governance system for preventing public health risks of major infectious diseases has become an urgent demand of the country and the people. The Chinese government has always attached great importance to the prevention and governance of infectious diseases. The Outline of the “Healthy China 2030” Plan clearly points out that “strengthen the prevention and control of major infectious diseases,improve the monitoring and early warning mechanism of infectious diseases,strengthen the prevention and control of sudden acute infectious diseases,and actively prevent imported sudden acute infectious diseases”,which clarifies the necessity and feasibility of strengthening the prevention,control and governance of infectious diseases from the perspective of national strategy. The Opinions of the State Council on Implementing the Healthy China Action also provide relevant guidance for infectious disease prevention and control from a methodological perspective. Therefore,studying the public governance issues of infectious diseases and the role division of governments at all levels in the governance process has strong practical significance.

Under the framework of the Chinese style fiscal decentralization system,local governments need to develop their local economy while also taking into account their responsibilities to ensure people's health and resist major infectious disease risks. Therefore,it is necessary to explore the role of fiscal decentralization in the process of infectious disease governance and clarify the division of governance roles among governments at all levels. Due to the relatively limited research on fiscal decentralization and infectious disease governance,this article will have the following marginal contributions. Firstly,geographic space is a very important factor in the study of infectious disease issues,but there is little mention in existing literature. This article will consider the impact of spatial geographic relationships in the research. Secondly,what role should fiscal decentralization play in the process of infectious disease governance,that is,what powers and expenditure responsibilities should the central and local governments respectively bear in infectious disease governance,and where are the boundaries of powers?This question does not have a good answer. This article will use theoretical model derivation and empirical regression analysis to decompose and differentiate the effectiveness of fiscal decentralization in infectious disease governance. Thirdly,in the face of the current issue of the spread of infectious diseases in China,how should governments at all levels respond to the situation? This article will provide corresponding answers through specific policy recommendations.

In view of this,this article first establishes a theoretical model of the spatial distribution and governance effectiveness of infectious diseases from the perspective of fiscal decentralization,and theoretically confirms the actual division of powers in the process of infectious disease governance and proposes corresponding hypotheses. Secondly,taking the incidence of infectious diseases in Chinese mainland as the research object,the spatial Durbin model is used to construct the corresponding spatial weight matrix,and the spatial econometric model is used to estimate the spatial impact of fiscal decentralization on the incidence rate of infectious diseases. Furthermore,this article gradually decomposes the direct,indirect,and total effects of fiscal decentralization on infectious disease governance,and evaluates and explores the division of responsibilities between central and local governments in the process of infectious disease governance based on the decomposition results. Then,by establishing a panel threshold regression model,explore the boundary division of power between central and local governments in infectious disease governance. Finally,the endogeneity problem of the benchmark model is solved through the instrumental variable estimation method. The robustness test is carried out from two aspects:the difference of fiscal decentralization measurement indicators and the different setting of spatial weight matrix. The heterogeneity analysis is carried out from two aspects:the regional difference and disease type difference of infectious disease governance,and conclusions are drawn and relevant policy recommendations are put forward.

Based on the above conclusions,this article draws the following policy recommendations:Firstly,local governments should establish a sense of governance that values residents' health and infectious disease management,and make it an important content for local government performance evaluation and official rating assessment,so that the “Healthy China” strategy can be reasonably implemented. Secondly,give full play to the information advantages of local governments in understanding the preferences and needs of local residents,formulate corresponding epidemic prevention policies and epidemic management regulations according to the characteristics of different regions,control the source of infection of infectious diseases and cut off Pathogen transmission. Properly improve the financial decentralization in the field of infectious disease governance,and minimize the incidence rate of infectious diseases. Thirdly,we should attach great importance to the governance of infectious diseases from the source,and carry out scientific governance during the critical period of infectious disease prevention and control,never sacrificing the health of the people for temporary economic growth;encourage all sectors of society and the media to supervise the governance process of infectious disease control,and eliminate the possibility of local governments concealing or missing reports,confusing the public.

From the perspective of unexpected cash flow shocks in the market,this study explores the source of profitability anomalies in the Chinese A-share market.We hypothesize that the expectation bias for a firm's profitability information leads to unexpected firm's future cash flows,and the cash flow shocks thus give rise to the variations in stock returns,which appear as the differences in cross-sectional returns among firms with different levels of profitability.Specifically,profitability shocks can positively explain profitability anomalies,and the higher (lower) the profitability shocks of firms are,the unexpected firms' profitability is higher (lower),the more (less) the firm's profitability are undervalued,thus generating a higher (lower) stock return.

To investigate whether cash flow shocks can explain profitability anomalies,we use the expected profitability from a cross-sectional profitability model to proxy for the expected cash flows.The major advantages of the cross-sectional profitability model include the substantially wider cross-sectional and time-series coverage,lower levels of forecast bias and more precise earnings response coefficients; thus,it can capture a large portion of the variations in expected profitability across firms.Then,as the proxy for cash flow shocks,we calculate the profitability shocks by taking the difference between the realized profitability and the expected profitability based on the cross-sectional profitability model.

It's important to note the meaning of profitability shock is different from the notion of expectation error for profitability or earning surprise in the existing literature.First,the theoretical basis of profitability shocks based on present-value model suggests a firm's stock returns are driven by shocks to expected cash flows and/or shocks to discount rates.Second,the profitability shocks can objectively reveal the profitability deviations by using firms' operating information that can forecast the firms' future profitability for cross-sectional regression.Third,the explanation of cash flow shocks is more in line with the fact that the profitability anomalies can be stable and persistent,dispelling the argument about the paradox of mispricing for firm's profitability in behavioral finance.

The reasons that we take the Chinese A-share market as the study sample can be summarized as follows.First,the Chinese stock market is the most rapidly growing and upgrading market worldwide.However,investors' trading philosophy,information environment,and system design in the Chinese stock market are quite different from those of mature stock markets,and these unique factors prominently affect the asset pricing mechanism.Second,firm profitability in China is more unstable than that in developed countries,making it  more difficult for the ex ante expectation of firms' future profitability,thus inducing a higher likelihood of unexpected profitability shocks.

To ensure the reliability and robustness of the results,our analysis of profitability shock explanation for profitability anomalies proceeds in three steps.First,we provide solid evidence of the profitability anomalies in the Chinese A-share market and analyze the relationship between firms' profitability and profitability shocks.We employ multidimensional indicators of profitability—CBGP (Cash Base Gross Profitability),GP (Gross Profitability),OP (Operating Profitability),and ROA(Return on Assets).Second,we explore the source of profitability premium by double sorts on profitability and profitability shocks,regressions of profitability portfolio returns on the mimicking profitability shock factor,and the profitability premium adjusted by profitability shocks.Third,we conduct several robust checks to dispel the doubt that the explanatory power of cash flow shocks for the profitability premium is unstable.

The empirical evidence indicates that there is a notable positive relationship between the profitability premium and profitability shocks and that the latter can significantly explain the former in a positive way.Furthermore,the profitability premium measured by ex ante expected returns disappear or significantly declines if we remove the impact of profitability shocks.In addition,the stability of profitability anomalies and the explanatory power of profitability shocks are due to the persistence of profitability shocks,which allow the explanatory effect of profitability shocks to last for 24 months.Our investigation provides a new perspective to understand the mechanisms of the profitability premium,which enriches the literature on profitability anomalies.

The contribution of this paper can be summarized as follows.First,we explore the source of profitability anomalies from a new perspective,namely,the component of the  market's unexpected cash flow shocks in unexpected stock returns.The results highlight the importance of in-sample cash flow shocks for understanding cross-sectional return variation caused by firm profitability.The traditional explanations regarding mispricing focus on the micro individuals' expectation biases on stock future profitability information,and are related to forecast errors just using of the simple extrapolation for past earnings or the inaccurate analysts' forecasts for firms' profitability in time-series.Furthermore,the explanation based on irrational mispricing cannot dispel the arguments about the stable and persist existence of profitability anomaly.However,the explanation of cash flow shocks measures the market's systematical expectation biases on stock future profitability information,which explores the profitability premium from a wider market dimension.Most related to our work is Hou and Van Dijk (2019),who highlight the importance of profitability shocks in understanding the size effect.Our work extends their work by digging the explanatory role of profitability shocks for profitability anomalies.Second,our paper enriches recent literature that develops alternative proxies of expected returns to be used in asset pricing tests.We employ ex ante expected returns by adjusting unexpected cash flow shocks for ex post realized returns,which exclude the noisy interference of unexpected returns.Finally,combined with the unique market features in the Chinese stock market,the explanation of profitability shocks based on the market's systematical expectation bias for firm profitability provides some insight for understanding the asset pricing mechanism of this emerging stock market.

The digital economy has developed rapidly in recent years and has become an important  economic development engine  in the new era.Data,as a strategic resource for the development of the digital economy,in which enterprises can release massive amounts of data information, are playing an important role in data sharing, openness, and development and utilization.However, in recent years, frequent data leakage incidents have exposed a series of issues such as inadequate supervision and imperfect sharing mechanisms in data sharing.Given that data has become a strategic resource driving enterprise value creation,governments around the world are paying more and more attention to the governance of the enterprise data market.In May 2018,the EU enforced the General Data Protection Regulation (GDPR),which sets out the standards for personal data protection and requirements for enterprise data sharing.In addition,the EU is actively exploring a data pooling mechanism to facilitate data sharing among enterprises.China's Development and Reform Commission (DRC) and many other departments have also encouraged all types of subjects to voluntarily participate in data element sharing by issuing relevant documents and establishing laws and regulations to promote data sharing.The aim is to mitigate privacy risks and hazards and to enable openness and sharing of data.

This study considers big data alliances, members of big data alliances, and relevant government departments, and conducts a theoretical analysis of the effective sharing of decision-making impact data among various entities based on the theory of tripartite evolutionary games.In view of the problems of free-riding by big data alliance members and data leakage caused by big data alliance malfeasance in the process of data sharing,this study constructed a three-party game model to explore the decision-making choices of the big data alliance,big data alliance members,and relevant government departments in data governance,analyzed the stability of the three-party evolution game,judged the stability of equilibriums,combined with the simulation analysis of the data to test the impact of the factors on the stability of strategies,and verified the robustness and effectiveness of the evolutionary results from the theoretical level.factors on the stability of the strategy,verified the robustness and validity of the evolutionary results,and analyzed the mechanism and potential impact of these speculative behaviours from the theoretical level.

The research results indicate that:① Although the probability of big data alliance leakage increases as the value of data information among members of the big data alliance increases, the government's regulatory approach of allocating margin benefits still has a certain degree of governance effectiveness.② The decision-making of big data alliances is difficult to be influenced by social gains or losses, and the diverse governance at the social level lacks influence on the choice of game strategies.③ Members of the Big Data Alliance are highly sensitive to the margin system, and this measure as compensation for data governance can effectively alleviate the phenomenon of free riding among members of the Big Data Alliance.④While evaluating governance measures to improve the data market, the government also needs to measure whether the distribution of profits from margin is fair and reasonable.The tripartite evolutionary game model constructed in this article can provide a certain reference basis for improving the data governance mechanism and promoting the sustainable development of the data-sharing market.These results we got show that the government's adoption of the margin system has a certain constraint on the decision-making choices of big data alliances,and the government should formulate the margin system reasonably and ensure that the allocation scheme of the margin is scientific and appropriate.The members of big data alliances are more sensitive to the security deposit levied by big data alliances,so big data alliances should optimze the security deposit system to cope with the free-riding behaviour of their members.It is difficult for big data alliance to be influenced by external gains and losses brought by the social level in its decision-making process,so it is difficult to influence the choices of big data alliance by influencing its corporate reputation through social opinion,etc.In addition to the government's deposit system,strengthening the construction of internal self-governance mechanism is also one of the effective paths.If a member of a big data alliance leaks information about its members' data,it will quickly attract the attention of the relevant government departments and be governed,while the free-riding behaviour of the big data alliance members is difficult to be detected by the government in a timely manner.Therefore,the internal regulatory mechanism can be strengthened to give full play to the governance effectiveness of big data alliances.

The research in this paper treats the data shared by big data alliance members as homogeneous and does not further distinguish between the difference in data costs of members upstream and downstream of the supply chain as well as the cooperative relationship between members.Therefore,it is the next research direction to consider including both upstream and downstream enterprises of big data alliance members in the model,constructing an evolutionary game model under the participation of upstream and downstream members,and investigating the mechanism of bargaining power in the negotiation of cost-benefit distribution in the co-operative governance among the members on data sharing,so as to put forward constructive suggestions for the realization of effective data governance.

Entrepreneurial networks play a pivotal role in the viability and advancement of startups.The introduction of the network agency perspective has directed research attention toward entrepreneurial networking,investigating the question of “where does the entrepreneurial network come from”,i.e.,how entrepreneurs establish and develop networks.Entrepreneurial networking,in essence,encompasses a series of behaviors,activities,and means undertaken by entrepreneurs to form and develop networks with external stakeholders.

However,previous studies on entrepreneurial networking remain in a fragmented stage of strategy exploration,failing to elucidate the underlying theoretical assumptions of different entrepreneurial networking strategies comprehensively.Besides,the conceptual connotations of entrepreneurial networking and the decision logic behind it have not been clearly identified.Specifically,following different theoretical assumptions such as “design-precedes-execution”,“execution-precedes-design” and “convergence of design and execution”,entrepreneurial networking exhibits differentiated modes and strategies.Meanwhile,the exploration of antecedents of entrepreneurial networking in existing research is still in its nascent stage,making it difficult to fully explain the reasons why entrepreneurs adopt distinctive networking modes.Hence,this paper coordinates various underlying theoretical assumptions of entrepreneurial networking and systematically analyzes its conceptual connotation.Then,we take the decision logic of entrepreneurs into account and summarize three paths concerning the “decision logic-networking mode” relationship.

Based on systematic collection and analysis of existing literature,this paper demonstrates a path framework of entrepreneurial network formation.First,according to its theoretical assumptions,entrepreneurial networking is divided into three modes,namely “design-execution” mode,“execution-design” mode,and “design×execution” mode.Specifically,the “design-execution” mode regards entrepreneurial networking as a goal-oriented activity and a strategic and instrumental resource-seeking action,while the “execution-design” mode takes entrepreneurial networking as an inspiring entrepreneurial network-building action,which provides basis for the formation of goals of entrepreneurial action.Combining the two assumptions,the “design×execution” mode treats entrepreneurial networking as actions based on a vague and dynamically adjusted plan,with an orchestration of people,resources,and ideas.

Second,grounded in the cognitive perspective,this paper initiates a comprehensive framework for entrepreneurial networking,discerning three source paths of entrepreneurial networks.First,in network collecting path,entrepreneurs adopt the causation logic and are driven by goals,strategically selecting valuable network relationships,making plans,and establishing network relationships with target partners.During such process,entrepreneurs face a risky context,and entrepreneurial networking is essentially about finding the best means to achieve the established goal.And the final structure and form of the entrepreneurial networks depend on the goals set by the entrepreneurs in advance.Second,in the network convergence path,entrepreneurs adopt the effectuation logic,combining internal factors such as personal experience and ability with external conditions such as accidents to take different behaviors to develop the entrepreneurial network.During such process,entrepreneurs will give full play to their subjective initiative,utilize contingency to control new situations,and even create new means to strive for the best possible results.The structure and form of the entrepreneurial networks are jointly created and defined by entrepreneurs and their partners without possibility to be predicted in advance.

Third,in the network “collecting+convergence” path,entrepreneurs adjust their decision logics and networking strategies according to the level of situational uncertainty throughout the life cycle of the enterprise development,taking analytical and planned networking actions to integrate the entrepreneurial network while remaining open to  contingency. They usually connect with partners in an inspiring way and gradually refine entrepreneurial networks while focusing on achieving their long-term goals.Then,taking regional institutional environment,stage of entrepreneurial enterprises,pre-existing network characteristics and entrepreneurs' previous experience as examples,this paper discusses the moderating effects of uncertainty on the above paths of entrepreneurial network formation.

Eventually,future research directions are proposed from three aspects:refining the concept of entrepreneurial networking,exploring new issues in digital context,and expanding research methods.First,future research is encouraged to conduct in-depth exploration of the process mechanism of dynamic evolution of the “collecting+convergence”path.For example,dynamically characterizing the behaviors of entrepreneurial networking driven by various decision logics.Second,based on the characteristics of the digital organizations,the unique concept,strategies and mechanisms of platform-based entrepreneurial networking are under exploration.Third,it is recommended to apply new research methods such as machine learning and large models to effectively utilize digital platform big data and capture entrepreneurial' networking strategy and its mechanisms.

This paper contributes to entrepreneurial network research by summarizing various modes of entrepreneurial networking and proposing a path framework concerning “cognition-behavior” framework.Hence,this research helps to enrich the understanding of entrepreneurial networking and explore the antecedents of entrepreneurial networking strategy to understand why and how entrepreneurs adopt distinctive networking strategies from a more integrated perspective.Moreover,this review helps entrepreneurs understand the conceptual connotation and strategies of entrepreneurial networking more comprehensively and guides them to adopt appropriate strategies based on various decision logics,so as to construct their entrepreneurial networks effectively.

Equity crowdfunding is a new financing mode with the advantages of low threshold,high efficiency,and low cost.It provides a financing channel for small and medium-sized enterprises and absorbs the idle funds of the public.However,the emerging mode also faces many problems,such as legal environment,credit system,intellectual property protection,and social cognition,which affect the high-quality development of enterprises and pose risks to investors’ decision-making.Therefore,this article aims to explore the key factors that affect the post-crowdfunding financing performance of equity crowdfunding enterprises,based on the signal theory and using the data from Seedrs equity crowdfunding platform and Crunchbase.We examine the impacts of project characteristics,crowdfunding performance,and advertising effect on the subsequent financing performance of start-ups that completed equity crowdfunding.The subsequent financing performance includes three aspects:whether they can enter the next round of financing,the time required for the next round of financing,and the amount of the next round of financing.

Results show that project characteristics (such as project valuation,target amount,equity share,and policy preference),crowdfunding performance (such as previous financing experience),and the advertising effect (such as the number of investors),all have significant positive effects on the subsequent financing performance(Whether they can enter the subsequent financing, the time required for the subsequent financing or the amount of the subsequent financing).In addition,video introduction length,city level,company operation object,digitalization level,financing completion time,and company age also have certain impacts on the subsequent financing.

In specific,we review the existing literature on crowdfunding,equity crowdfunding,and private equity,and summarize the research gaps and contributions.It points out that most of the existing literature focuses on whether the equity crowdfunding projects can successfully raise funds,but lacks a detailed and reasonable study on whether the financing enterprises can successfully enter the next round of financing,and the subsequent financing performance of the enterprises.

We further propose the research hypotheses based on the signal theory,and analyze the impact of project characteristics,crowdfunding performance,and advertising effect on the subsequent financing performance.It argues that project characteristics,crowdfunding performance,and advertising effect can be regarded as information signals for investors,and the signals from these aspects will greatly affect investors’ decision-making and subsequent financing performance.The positive signals from these aspects can enhance investors’ confidence,attract more funds,and promote the long-term success of equity crowdfunding enterprises.

We collect and process the data from Seedrs equity crowdfunding platform and Crunchbase,and conduct descriptive statistics and regression analysis.The article selects 1,024 equity crowdfunding projects that were successfully completed on the Seedrs platform from January 2012 to December 2019,and matches them with the data from Crunchbase to obtain information of the subsequent financing rounds.Three dependent variables are defined to measure the subsequent financing performance:whether the project can enter the next round of financing,the time required for the next round of financing,and the amount of the next round of financing.Several independent variables are constructed to capture the project characteristics,crowdfunding performance,and advertising effect.We control for some other variables that may affect the subsequent financing performance,such as video introduction length,city level,company operation object,digitalization level,financing completion time,and company age.

The article reports the main results of the regression analysis,and discusses the implications and limitations.The article finds that:

(1)Project characteristics have significant positive effects on the subsequent financing performance.Specifically,project valuation,target amount,equity share,and policy preference all have positive effects on whether the project can enter the next round of financing,the time required for the next round of financing,or the amount of the next round of financing.These results suggest that project characteristics can send positive signals to investors about the project quality and potential return,and affect investors’ evaluation and decision-making on the project.

(2)Crowdfunding performance also has significant positive effects on the subsequent financing performance.Specifically,previous financing experience has a positive effect on whether the project can enter the next round of financing,and the amount of the next round of financing.This result suggests that previous financing experience can send a positive signal to investors about the project’s credibility and ability,and affect investors’ trust and confidence in the project.

(3)Advertising effect also has significant positive effects on the subsequent financing performance.Specifically,the number of investors has a positive effect on the amount of the next round of financing.This result suggests that the number of investors can send a positive signal to investors about the project’s exposure and reputation,and affect investors’ perception and attention to the project.

The article concludes by summarizing the main findings,contributions,and implications,and pointing out the limitations and directions for future research.The article argues that this study provides a systematic empirical analysis of the post-crowdfunding financing performance of equity crowdfunding projects,and reveals the key factors and mechanisms that affect the subsequent financing performance from different aspects.The article also argues that this study has important implications for equity crowdfunding platforms,start-ups,and investors,as well as for policymakers and regulators.The article acknowledges that this study has some limitations,such as the data source,the measurement of variables,and the causal inference,and suggests that future research can further explore the impact of equity crowdfunding on the long-term development and survival of start-ups,and the role of social networks and institutional factors in equity crowdfunding.

The securities industry is widely recognized as one of the most data-intensive sectors,characterized by diverse business scenarios.However,due to stringent regulations and high entry barriers,the growth of new securities companies is sluggish.Nevertheless,competition among the industry is intensifying.In order to expand their market share,securities companies have employed various strategies to attract new customers.They have invested considerable effort in providing personalized marketing services to enhance their customer relationship management capabilities.However,a top priority for securities companies in their customer relationship management efforts is retaining existing customers and preventing potential customer churn.

This research focuses on customer churn in the securities sector.The data used in this study consists of user-level transaction data provided by a prominent domestic securities company.Initially,we conduct data inspection and categorize the raw data into asset variables and non-asset variables.After cleaning the original data and filtering out relevant variables for later analysis,we define customer churn based on the stability of the churn status,drawing from practical experience within the securities industry.We then investigate how the number of trading days,logins,and assets impact the state of customer churn,ultimately arriving at a viable and effective definition of customer churn.Having established the response variable,the subsequent crucial task is to identify meaningful impact factors from hundreds of raw variables.To address this,we propose an independent screening method based on high-dimensional features.We first divide the churn factors into asset variables and non-asset variables,each represented by 8 and 4 aspects,respectively.From these aspects,we derive a series of indicators.We also calculate the ratios or products of asset and non-asset factors to obtain 10 composite churn factors.Subsequently,we employ the univariate AUC method,considering data quality and actual business context,to screen the churn factors.As a result,8 asset-type factors,21 non-asset-type factors,and 2 compound factors are selected for modeling.

Building upon the logistic regression model framework,we propose separate daily and weekly customer churn models,both based on the screening features.The results demonstrate that both asset-type and non-asset-type variables significantly influence customer churn prediction.The daily customer churn prediction model achieves an AUC above 0.95,indicating strong prediction accuracy.Moreover,compared to the daily model,the weekly average model exhibits lower computational cost,higher prediction efficiency,and greater stability.The model also reveals underlying mechanisms behind customer churn.For example,the coefficient of the maximum value of stock fund option market capitalization is negative,indicating an inverse correlation between the maximum value of stock fund option market capitalization and customer churn.A lower maximum value of stock fund option market capitalization might suggest poor investment performance or dissatisfaction with the current market situation,thereby increasing the likelihood of customer churn.

Regarding age,the probability of customer churn follows the pattern:60 years and above>50~60 years>40~50 years>below 40 years.This suggests that older customers are more prone to churn.Particularly,customers aged between 50 and 60 years are more likely to churn,possibly due to their proximity to retirement age,leading to higher financial planning and retirement considerations.Among the age groups,customers aged 60 years and above are most susceptible to churn.This could be attributed to changes in financial planning and service requirements after retirement or other factors that make them more likely to transfer their investments or switch service providers.

Our research findings can provide strategic analysis for companies to recover customers.Based on the proposed daily customer churn model,it is possible to calculate the predicted churn probability value for each customer and sort the samples in descending order according to the prediction values.We introduced the coverage-capture rate curve to evaluate the model's prediction accuracy in practical business scenarios.Based on the modeling results,we classified customers into ten categories.In real-world business operations,companies can develop specific recovery strategies for customers with different churn risks.For instance,they can focus marketing budgets on high-risk churn customers,engaging in face-to-face visits,phone follow-ups,and direct communication with customers.By gaining targeted insights into customer status and preferences,they can implement appropriate measures such as reducing commissions or offering more convenient services,effectively minimizing the risk of customer churn.

Finally,we applied the customer churn model based on high-dimensional features proposed in this paper to the online production environment of the enterprise,which enables real-time identification of customer churn status.To achieve this,we conducted tests in collaboration with a partner company on over four million valid customers from September to December 2020.To validate the accuracy of the online model,we randomly sampled 5% of these four million valid customers as the testing sample.During the online testing,the performance was excellent,with a prediction accuracy of 39.2% and a recall rate of 93.6%.For the enterprise,having a higher recall rate is particularly crucial because a higher recall rate indicates a higher probability of capturing truly churned customers from the original sample.

The growth of the platform economy in China has accelerated notably,with its influence on broader economic and societal progress becoming increasingly discernible. As internet traffic underpins the platform economy,its relationship therewith necessitates immediate scholarly inquiry. This study initiates with an overview of the historical trajectory of China's platform economy and the evolution of its regulatory framework. Subsequently,it investigates the consequences of monopolistic platforms' traffic-driving strategies on social welfare,focusing on the decision-making processes of diverse market participants within the platform economy. 

Utilizing a Hotelling-based model of platform monopoly,the research reveals several insights. In the proposed model,two pivotal design elements are instantiated: firstly,the platform facilitates the generation of traffic by means of deploying advertisements pertaining to enterprises' commodities,thereby directing novel users to the firms; secondarily,the platform can set the price for user traffic. We solve for the equilibrium conditions of the model within a specific parameter space and examine the manner in which the platform's pricing mechanisms and the strategic behavior of firms are modulated by the level of market segmentation. We find that,in scenarios where monopolistic platforms employ “paid traffic-driving” as a strategy,prospective market entrants opt to remunerate the platform for marketing services thereby enabling market access,and the platform generates positive revenue. In instances where the level of market segmentation is low,the equilibrium of the model consistently exhibits a state of relative stability,with the firm's marketing strategy—specifically,the ratio of consumers reached through Internet traffic driving via the platform—remaining constant. Conversely,in scenarios characterized by a high degree of market segmentation,the firm's marketing strategy intensifies correspondingly with the progressive fragmentation of the market,resulting in a monotonic increase in the percentage of consumers exposed to advertising for products.

Subsequently,the paper delves into the welfare economics implications of the model by incorporating the concept of a central planner and the definition of the “first-best” scenario. The analysis reveals that monopoly platforms manifest the fundamental characteristics with respect to welfare outcomes: monopoly platform fosters market competition and enhances consumer welfare,albeit partially,as the platform showing unsuitable goods to consumers counteracts welfare improvements. In barely segmented markets,variations in segmentation do not impact consumer welfare-related efficiency losses. However,in highly segmented markets,the monopolistic platform's actions result in more substantial efficiency detriments which increase with the degree of segmentation. 

The analysis of consumer welfare in this paper uncovers a more profound source of efficiency loss attributable to the platforms Internet traffic monetization practices based on traffic driving. Within the model proposed herein,consumers,who are inherently the suppliers of their traffic,lack the capacity of pricing; instead,it is the platforms or firms that retain the authority to set prices. Given that the cost of user traffic acquisition for platforms and firms is negligible (as no transfer payments are made to users),the optimal price of user traffic from the perspective of consumer welfare maximization should also be zero. From this perspective,the monopoly of the firm in the primary market can be decomposed into two parts: the firm initially secures a monopoly over traffic and monetizes it through commodity sales,then bars competitors from the market by setting the price of traffic driving at positive infinity. The emergence of platforms altered this. The platform achieves traffic monopolies and monetizes its traffic through traffic driving,thereby reducing the price of user traffic to a relatively lower level,which,in turn,enhances consumer welfare when compared to the scenario of complete monopoly by firms. Nevertheless,efficiency losses persist as the price of user traffic has not been restored to a reasonable level. These findings suggest that if consumers are precluded from pricing their own traffic,then Internet traffic should function to augment consumer welfare by disrupting monopolies and fostering competition among firms,rather than serving as a conduit for profit for those entities wielding traffic pricing power.

In response to these findings,the paper suggests a regulatory approach aiming to mitigate the adverse effects of monopolistic traffic driving,which implements interventions based on the distinctive attributes of the markets,for instance,the level of market segmentation,in which platforms operate.

Since the initiation of China's reform and opening-up,the nation has made remarkable strides in its socialist modernization efforts.In the early stages,rapid economic and social development was prioritized to meet the immediate developmental needs,emphasizing industrial growth and urbanization to lift large segments of the population out of poverty and underdevelopment.This approach was aligned with the urgent need to build a robust economic foundation.However,as the economy advanced and reached higher levels of development,the focus shifted towards optimizing overall developmental efficiency and addressing imbalances to prevent the “wooden bucket effect”, where the weakest link constrains progress.Among these imbalances,the urban-rural divide remains a significant challenge,with substantial disparities in development,public services,and income distribution between urban and rural areas.These disparities not only hinder overall national development but also exacerbate social inequalities and tensions.Reducing these disparities is crucial for achieving coordinated development and resolving the fundamental contradictions in Chinese society,ensuring that the benefits of modernization and economic growth are more evenly distributed across different regions and communities.

Cadres with experience as county party secretaries typically possess a profound understanding of grassroots conditions,strong connections with the populace,especially farmers,and a deep insight into urban-rural disparities.This uniquely positions them  to facilitate coordinated urban-rural development.Their hands-on experience at the county level enables them to identify and address the specific needs and challenges faced by rural communities,thereby fostering a more inclusive growth model.Therefore,this research examines the impact of county governance experience on the governance performance and policy approaches of prefectural party secretaries,focusing on their effectiveness in promoting urban-rural coordination and narrowing the urban-rural income gap.By evaluating how these leaders leverage their grassroots experience to implement policies that bridge the urban-rural divide,this study aims to provide insights into effective governance strategies that support balanced and sustainable development.

This study investigates the impact of county governance experience on prefectural party secretaries governance performance and policy approaches in promoting urban-rural coordinated development and reducing the urban-rural income gap.Utilizing prefecture-level annual panel data from 2001 to 2019,we conduct an empirical analysis.Our findings indicate that prefectural party secretaries with prior experience as county party secretaries are more effective in narrowing the urban-rural income gap in their jurisdictions.This result remains robust across various empirical models.We also perform a parallel trend test within the Difference-in-Differences (DID) framework proposed by Liu et al.(2022),confirming the causal significance of our findings.Heterogeneity analysis of work experience reveals two key insights:First,there is an additive effect of grassroots governance experience at different levels.Secretaries who have also served as township party secretaries achieve more pronounced results in reducing the urban-rural income gap.Second,the type of county-level administrative experience matters.Serving in counties within the same prefecture,non-municipal districts,and major agricultural counties has a more significant impact.Regional heterogeneity analysis shows that if a prefectural party secretarys jurisdiction includes national-level poverty counties,their county governance experience significantly enhances their ability to narrow the urban-rural income gap.This reflects the matching effect between officials work experience and regional development endowments.Micro-level household data analysis indicates that one specific mechanism driving this effect is the improvement of agricultural production and income for rural residents.Regarding specific policy measures,prefectural party secretaries with county governance experience tend to place a higher emphasis on agricultural work in their government reports.This leads to higher levels of agricultural modernization,improved agricultural production standards,and significantly better rural financial supply.

This paper makes two significant contributions to the literature.First,it examines the impact of grassroots work experience on prefectural party secretaries governance performance and policy approaches,aligning with studies on leaders growth,education,and career experiences.Based on “imprinting theory”, which suggests that formative environments have lasting effects (Simsek et al.,2015),the study explores how pre-career experiences influence policy decisions.For example,secretaries with educated youth experience or those who grew up in impoverished areas tend to invest more in rural development (Du and Xu,2019; Deng,2023).Additionally,higher education levels and international experiences enhance leaders capabilities,such as attracting foreign investment (Gao and Dong,2017),while local governance experience influences policy preferences and effectiveness in regional development (Persson and Zhuravskaya,2016; Zhou et al.,2020).This research fills a gap by focusing on the experience as county party secretaries,providing empirical support for the effectiveness of progressive cadre training,and offering theoretical and policy insights for cadre selection and development.

Second,by investigating the effect of county governance experience on urban-rural income gaps,this study contributes to the literature on urban-rural gaps,income distribution,and rural revitalization.Previous researches have highlighted urban-biased policies as  key factors in widening these disparities (Zhou and Zhou,2011; Chen and Lin,2013).This paper examines broader policy choices and governance behaviors,tracing them back to leaders career experiences.The findings suggest that leaders with county governance experience are better positioned to promote coordinated urban-rural development and narrow income gaps.This research provides valuable insights for policy-making aimed at rural revitalization,achieving common prosperity,and advancing Chinese-style modernization,by focusing on the rural perspective in policy formulation and implementation.

The prediction of financial distress among listed companies has perennially been a focal point in financial research.The scientific model is conducive to preventing financial distress and improving the early warning management of the crisis.From a methodological perspective,the financial distress prediction problem can be framed as a binary classification issue,where company information acts as explanatory variables for the prediction model.The output is binary,with 1 indicating a company facing financial distress and 0 indicating a company not facing financial distress.Among various financial distress prediction methods,the logistic model is widely used due to its advantages of simple calculation and straightforward coefficient interpretation.

Hambrick and Mason (1984) suggested that the psychological factors such as internal cognition,emotions,and values of executives determine their decision-making behavior,thereby significantly impacting business management,financial condition,and future development.With China’s  rapid economic development,enterprise investment,mergers and acquisitions,and group operations have resulted in an increasing number of directors,supervisors,and senior management personnel concurrently holding positions in two or more enterprises,forming a chain network of executives.The executive chain network embeds the network in the enterprise through the connection of executives,which makes enterprises more and more closely related and has a significant impact on enterprise behavior and performance.Hence,it becomes imperative to integrate the executive chain network into the model when predicting the financial distress of listed companies.However,existing financial distress prediction models have largely overlooked the impact of the executive network.

We propose a logistic model that incorporates prior information regarding the sample’s network to handle data with a network structure.We categorize variables into structural variables and non-structural variables based on whether their coefficients are influenced by the network structure.The coefficients of structural variables are allowed to vary across different samples.The similarity of the structural variable coefficients corresponding to the connected samples in the sample network is encouraged to be similar by the Laplacian quadratic penalty function.The first part of objective function is the negative log-likelihood function,and the second part is the Laplacian quadratic penalty function.The tuning parameter is selected by five-fold cross-validation.If the tuning parameter is 0,objective function reduces to traditional logistic method.The prediction process involves three steps.Firstly,the model is trained based on the samples and sample networks of the training set,and we get the estimation of the coefficients of non-structural variables and the structural variables corresponding to the training set.Secondly,the coefficient of the structural variables corresponding to the new samples is first calculated based on the sample network.Finally,the explanatory variables and estimated coefficients are utilized for prediction.

Section 3 presents simulation studies to evaluate performance of the proposed method.The proposed method is compared with traditional logistic models,neural networks (NNets),random forests (RF),support vector machine models (SVM1 for sigmoids and SVM2 for polynomials),and decision tree models.Monte Carlo simulation results demonstrate that the proposed method performs better than other methods.This suggests that considering the sample network structure can improve the effectiveness of parameter estimation and prediction for new samples.Furthermore,it is evident that in cases where the sample size is small and the variables possess a special structure,conventional black box models exhibit subpar performance.

We employ the proposed method to forecast the financial distress of listed companies.To ensure the availability of data,listed companies marked with “*ST” and “ST” are treated as samples of financially distressed companies.Using the data of listed companies from year t-2 to predict whether they will be marked with “*ST” and “ST” in year t.The explanatory variables serve as the foundation of the entire predictive model,and the selection of scientifically sound indicators is paramount.We select 38 indicators,and the specific indicators are detailed in Table 1,with descriptive analyses provided in Table 2.The data used to construct executive network is sourced from CSMAR database.If two listed companies share at least one identical executive,they are deemed connected in the network.The prediction results show that the prediction performance of the proposed method is significantly better than others.Thus,incorporating the executive network into the model can improve accuracy.In order to analyze the coefficient estimation,we established the proposed method and the traditional logistic model based on the entire sample dataset.The estimated values corresponding to these two methods are depicted in Figures 7 and 8.

In future research,there is potential to integrate more intricate network structures into financial distress prediction models,while employing variable selection methods to handle high-dimensional data effectively.Additionally,this study primarily focuses on the logistic model with a sample network structure,which could be expanded to include other models such as multi-class logistic regression and Poisson regression.Exploring the application of different models across various fields would be a valuable avenue for further investigation.

Banks can provide illiquid loans to enterprises,which are financed by the demand deposit which allows depositors to withdraw funds at any time,that is,liquidity creation.However,the potential mismatch of liquidity in the banks' balance sheet caused by liquidity creation may lead to bank runs.It is obvious that households will tend to withdraw funds when they hold negative expectations about the banks.Therefore,bank runs have always been a major obstacle faced by banks and received significant attention from academia and governments despite the continuous improvement of financial regulation and supervision.An effective means to deal with bank runs is deposit insurance.However,it has been proven to have many limitations such as moral hazard in recent literatures,thus we try to find some macroprudential policies to address bank runs in this article.

This article aims to extend the Diamond-Dybvig model(DD model) and establish a bank run model that includes a household sector,a production sector,a banking sector,and a government sector.In period 0,financial intermediaries absorb savings from the household sector and make their investment decision at the end of this period; due to the news shock,the withdrawal decisions of the heterogeneous households determine whether a bank run occur,and the impatient household sector begins to consume in period 1; then all deposits are returned,and the patient household sector begins to consume in period 2.Compared with the DD model,banks can freely choose investment portfolios (liquid assets or loans) to maximize their profits and face the risk of early withdrawals by patient households in our model.In addition,this model introduces a government sector to analyze the impact of liquidity coverage ratio on bank run risk and social welfare loss when bank runs happen due to irrational expectations of household sector.Thus,this paper can provide theoretical and practical significance for the establishment of our country's macroprudential framework.

We find that banks can help form the optimal allocation of resources in the economy through liquidity creation.But when they choose risky investment decisions according to profit maximization,bank runs often occur due to insufficient liquidity,thereby destroying the optimal allocation of resources and forming a run equilibrium.As a result,the economy suffers an efficiency loss,and the welfare of households decreases sharply.The liquidity coverage ratio could prevent bank run risks from two aspects.On the one hand,liquidity coverage ratio could force banks to hold more liquid assets,thereby improving the bank's asset structure and directly reducing welfare losses caused by bank runs; on the other hand,liquidity coverage ratio could ensure that banks can demonstrate to all households that they can“provide household deposits which they commit at any time” when faced with early withdrawals by some households in period 1,which can help patient households form good expectations towards the bank,thereby reducing the impact of news shocks on early withdrawals by patient households.However,liquidity coverage ratio regulation is not cost-free.This article explores the optimal liquidity coverage ratio and finds that while liquidity coverage ratio helps increase banks' liquid assets,it also leads to the production sector receiving less funds,thus a loss in the efficiency of the economy.In addition,this paper further expands the model to explore the optimal liquidity coverage ratio when there is uncertainty in household heterogeneous shock and news shock.We find that when uncertainty exists,the liquidity coverage ratio set by the government to prevent bank runs needs to cover the size of households with early withdrawals under the upper bound of the shocks.Therefore,the bank's profitability would further decline in this case,which implies that the funds towards production will further decrease,and the efficiency of the economy further decline.

This article provides a general model framework based on the DD model that can be used for policy and welfare analysis and enriches the theoretical model of bank runs and macroprudential policy in China.Meanwhile,this article introduces a macroprudential policy,that is,liquidity coverage ratio,and explores the optimal ratio and transmission channel of this policy,providing theoretical enlightenment for the government to formulate and implement related macroprudential policies,which is of great significance in the future policy design.

Based on the theoretical mechanism of bank run risk and the macroprudential policy described by the DD model,this paper provides a potential model framework for future theoretical analysis of bank runs,macroprudential policies,and other economic policies to explore the effects of different policy combinations.In addition,this article does not introduce more complex information friction situations in the model.According to the existing literature,information friction is often one of the important factors that lead to bank runs.Therefore,how to introduce information friction into the theoretical models of bank run is also an important future research direction.

The mixed ownership reform aims to increase the efficiency of state-owned capital allocation and improve the corporate governance of state-owned enterprises. It is the current focus and key of China's state-owned enterprise reform. In October 2022,the 20th National Congress of the Communist Party of China (CPC) pointed out that “deepening the reform of state-owned capital and state-owned enterprises,accelerating the optimization of the state-owned economic layout and structural adjustment,promoting the state-owned capital and state-owned enterprises to become stronger and better,and enhancing the core competitiveness of enterprises”. Mixed ownership reform has continued to advance at different levels of the state-owned economy. With the continuous strengthening of mixed ownership reform,non-state capitals have taken shares in state-owned enterprises,which has brought significant impacts on the governance structure,business mechanism and operational efficiency of state-owned enterprises. Based on this,academics have explored the economic consequences of the mixed ownership reform of state-owned enterprises (SOEs),focusing mainly on the economic efficiency of SOEs in terms of investment activities,financing activities and enterprise performance.

In recent years,how to promote green economic and social development has attracted worldwide attention. The overall task of China's ecological civilization in the new era is to promote green development and harmonious coexistence between human beings and nature. However,existing researches on the consequences of mixed ownership in SOEs have paid more attention to the economic development of the enterprises,but less attention to the impact of the investment on environmental protection. The impact of mixed ownership reform of state-owned enterprises on environmental issues has been an important topic of widespread concern in both theoretical and practical circles. Based on this, the paper explores how the degree of mixed ownership affects the environmental investment of state-owned enterprises and how local government environmental regulation influences this relationship.

Focusing on the above issues,this paper uses the data of Chinese A-share state-owned listed companies as a sample to systematically investigate the relationship between mixed ownership and environmental protection investment. We find that the degree of mixed ownership is negatively correlated with environmental protection investment. The greater the diversity of mixed ownership and the higher the degree of integration,the less investment in environmental protection. We further consider the impact of the new Environmental Protection Law,which was implemented on 1 January 2015,and find that the relationship between the degree of mixed ownership and environmental protection investment remains negative before and after the implementation of the institution. This negative relationship is significantly alleviated after 2015,reflecting the importance of government environmental regulation. Therefore,we then explore the impact of government environmental regulation to further confirm the mitigating effect of government environmental regulation on the negative relationship between the degree of mixed ownership and environmental investment.

Further analysis indicates:distinguishing the types of environmental protection investment,the degree of mixed ownership has an inhibitory effect on different types of environmental protection investment; distinguishing the degree of industry pollution,the degree of mixed ownership has a stronger inhibitory effect in heavy polluting industries; distinguishing the participation of mixed entities,the higher the degree of foreign and private equity participation,the lower the investment in environmental protection; distinguishing the level of state-owned enterprises,the degree of mixed ownership in central state-owned enterprises has less impact. The conclusions of the paper remain unchanged after a series of robustness tests including the difference-in-differences model,Heckman two-stage model,firm fixed-effects model,adding control variables,and changing the environmental regulation measure.

The finding of this paper has the following contributions. First,previous studies on mixed ownership in SOEs have mainly focused on corporate performance,investment decisions,risk-taking,etc.,and few papers have paid attention to the impact of the degree of mixed ownership on environmental protection. Although most scholars have found that the degree of mixed ownership promotes the performance growth of SOEs,the performance growth does not mean the growth of environmental protection inputs,and it is even possible that the performance growth is at the expense of environmental protection inputs. This paper examines the impact of the degree of mixed ownership on environmental protection and broadens the study of the economic consequences of the degree of mixed ownership. Second,previous research on environmental protection has focused on environmental regulation,government regulation,social media and so on. Little literature has focused on the impact of changes in own equity structure. SOEs hold a large amount of economic resources and occupy a monopoly position in important industries affecting the environment,and the efficiency of SOEs in environmental protection largely determines the ultimate effect of environmental protection in China. This paper provides a new perspective for the study of environmental protection and highlights the importance of government environmental supervision,and also provides inspiration on how to synergistically improve mixed ownership and environmental protection.

The financial market plays a crucial role in supporting firms in advancing basic technologies. This study investigates how the investors in the A-share market in China value the firms with different layers of artificial intelligence (AI) technology. Specifically,we classify firms with different layers of AI technology into three groups-basic,mixed,and application-and examine the initial public offering (IPO) prices and the subsequent returns of these AI firms. 

Our sample consists of all AI firms that went public on China’s A-share market between 2007 and 2024. We identify the scope of AI firms by ①reviewing the “main business” and “business scope”sections of listed companies in the“cninfo.com”database to identify AI firms initially; ② reading“the main business of the issuer and changes since its establishment” in the IPO prospectuses to further identify AI business of these firms around the IPO time. The sample comprises 300 AI firms that conducted IPOs on the Shanghai and Shenzhen A-share markets,including the Growth Enterprise Market and STAR Market,from 2007 to 2024. Based on the Artificial Intelligence Standardization White Paper (2021) and ISO/IEC FDIS 22989 standards,firms are then classified into basic,mixed,and application layers. IPO initial day return is used to measure issuance pricing efficiency,and post-IPO long-term return after the initial day is used to measure price efficiency on the initial day.

Our empirical analyses reveal the following findings:①AI firms in the basic layer exhibit significantly higher returns on the IPO day,outperforming firms in the application layer by 106.4%. The evidence indicates relatively lower offering prices for firms in the basic layer; ② firms in the basic layer exhibit significantly higher post-IPO long-term returns after the initial day,with 20-day Buy and Hold Abnormal Returns (BHAR) higher by 11.5%,60-day by 19.7%,120-day by 18.4%,and 240-day by 20.8%. The evidence suggests the underpricing of firms in the basic layer on the initial day; ③ Firms in the basic layer possess more invention patents but fewer total,utility,and design patents,consistent with the explanation of higher information asymmetry in pricing basic technologies. Firms in the basic layer also show weaker pre-IPO profitability but higher future earnings growth,aligning with the explanation tied to historical issuing PE limits in China’s A-share market and investor myopia; ④ Registration-based reform significantly reduces differences in initial returns and post-IPO long-term returns after the initial day across the AI firms with different technology layers. It suggests that the reform improves pricing efficiency for firms in the basic layer. This improvement is associated with the removal of pricing restrictions under the reform and the shift of focus from profitability metrics to the quality of information disclosure.

Our study contributes to the literature by ① addressing the lack of research distinguishing basic,mixed,and application technology and examining the difference in valuation between the firms with different layers of technology and ② exploring the capital market’s support for the AI industry,a vital area for China’s economic growth. Additionally,this paper provides several practical insights for different stakeholders:① From the perspective of firms,basic technology can drive future financial performance and firms’ value growth. Therefore,companies should increase their investment in basic technology. At the same time,firms with basic technology should strengthen information disclosure to alleviate undervaluation. ② From the perspective of investors,since the IPO pricing of firms with basic technology remains relatively low,investors should enhance their understanding of the economic significance of basic technology and the value of related firms. It could facilitate the creation of higher investment returns. ③ From the perspective of policymakers,efficient pricing is important for the development of basic technology. The registration system reform has strengthened the capital market’s support for basic technology. Future policies should continue to optimize asset allocation and increase the efficiency of market pricing. The findings in this paper are based on the Shenzhen and Shanghai A-share markets in China,where the information environment and disclosure practices differ from those in the developed Western markets,which may have different IPO pricing strategies. Therefore,further research is needed to answer the question of  whether our findings can be generalized to other capital markets.

In this study,we examine persistence in the performance of activist short sellers in identifying firms to short.In contrast to prior work on mutual funds and security analysts,we first document that the average market-adjusted return of an activist's past targets does not predict the return of his/her current campaign target.Rather,we find evidence that the activist's past track record in identifying firms that delist predicts the likelihood that the target of the current campaign will delist.This finding holds even in a subsample of activists that survive.The evidence suggests that superior activist short sellers possess the consistent skill/ability to identify firms that delist,despite the lack of consistency in predicting targets' cumulative returns in pre-specified return accumulation windows.These seemingly conflicting results are nonetheless consistent with anecdotes that it is very difficult to consistently time shorts correctly due to the uncertainty  when prices will incorporate the bad news.

Consistent with persistence in the delisting measure of campaign performance,we find that investors react more negatively on the publication date to campaigns initiated by activists with a greater percentage of past targets that delist.We find that this more negative reaction is concentrated on the day the campaign is initiated; the market-adjusted return in the week or month after the publication date is not associated with the activist's past performance.Further,we provide preliminary evidence that there is a greater increase in the aggregate short interest before campaigns announced by activists with a superior record in identifying firms that delist.This result suggests the short side as a whole (activist short sellers,their clients,and/or friends) is able to capitalize off superior activists' greater market reputation.This ability in turn provides an incentive for activist short sellers to maintain their reputation for superior performance.

Finally,we investigate whether performance persistence varies with activists' tenure.We first document that the likelihood that an activist leaves the profession is more sensitive to past performance in the early years of his/her career,providing greater incentives for activists to perform well initially.Consistent with this result,we find that performance persistence is higher in the early years and declines as tenure increases,even after we correct for survivorship bias.Despite this decline in persistence with tenure,we do not observe that the market reaction to past performance varies with tenure.The evidence is consistent with activists' establishing a reputation when they first enter the profession,and either working less or becoming more opportunistic once experienced,e.g.,once their reputations reach very high levels as in Benabou and Laroque(1992).Overall,the paper sheds light on the behavior of activist short sellers and has broader implications in understanding the new form of  information intermediary in which investors act as intermediaries for each other.

In this study,we investigate the evolution of Sino-U.S. competition in the chip equipment industry,focusing on the dynamic interplay of technological nationalism policies. We aim to understand how the United States’ restrictive measures and China’s support strategies interact over time and impact technological development in this strategically critical sector. We observe that the world has entered an era characterized by technological geopolitical uncertainty,where a “tech cold war” is reshaping global competition. The United States employs technological nationalism to restrict China’s access to key technologies,leveraging tools such as the “entity list.” Meanwhile,China counters with policies like the National Integrated Circuit Industry Investment Fund (the “Big Fund”) to strengthen its domestic capabilities. Our study critiques existing research that treats U.S. restrictions as static,one-time events. Instead,we adopt a dynamic perspective,analyzing the evolution of these policies and their long-term implications for both nations. Using the semiconductor industry as a case study,we examine this interplay at the level of specific technological fields.

To achieve our goals,we use a knowledge graph approach. We decompose global semiconductor manufacturing patents into 88 technological fields,mapped based on their co-occurrence relationships. This allows us to explore how U.S. restrictions and Chinese support policies target specific technologies. By comparing the patent knowledge networks of U.S. entity list firms and Chinese firms supported by the Big Fund,we identify patterns of co-evolution. This analysis provides insights into the overlap and divergence between the two countries’ strategies.

We find that U.S. and Chinese policies are not static but evolve over time. The United States has adopted  increasingly specific restrictions,particularly during 2018—2020. However,these measures showed reduced continuity post-2020,influenced by shifts in political leadership. In contrast,China’s support policies through the Big Fund are remarkably consistent,focusing on building self-reliance in semiconductor design,manufacturing,and packaging. We also observe that the “needle-for-needle” adversarial strategy between the two countries peaked in 2020 and subsequently declined,suggesting adjustments in both nations’ approaches.

Our findings reveal that both countries prioritize policies targeting high-centrality technologies:Primarily targeting fields with high technological importance,particularly those critical to national security. Emphasize commercially promising technologies rather than exclusively addressing “choke-point” areas. Despite rhetoric about tackling weaknesses,we find that China invests more heavily in areas where it already has competitive strength.

Our analysis highlights that technological,market,and political factors shape policy decisions differently:Policies from both nations strongly focus on high-centrality fields in the knowledge network. Competitive fields attract more intervention from both countries. However,U.S. policies exhibit a greater focus on restricting scientific innovation,while China emphasizes supporting areas with immediate market potential. While U.S. measures show discontinuities influenced by political cycles,China maintains a steady trajectory of support,reflecting its centralized approach to industrial policy.

We find that U.S. and Chinese strategies exhibit a moderate degree of co-evolution. While both countries target similar technological areas,their approaches reflect different priorities. The United States emphasizes maintaining its dominance in critical areas,while China seeks to reduce its dependence on foreign technologies. Our results suggest that while the term “decoupling” implies complete separation,the reality is more nuanced. Both nations exhibit overlapping interests,leading to a complex dynamic rather than outright divergence.

We propose several avenues for future investigation:①Policy Drivers: Understanding the factors that influence countries’ choices of target technologies. ②Firm-Level Responses: Examining how firms navigate conflicting pressures from domestic support and foreign restrictions. ③ Micro-foundations of Decoupling: Analyzing how firm strategies and technological developments contribute to broader patterns of decoupling.

In conclusion,we have shown that Sino-U.S. technological competition is a dynamic and evolving process. We provide evidence of co-evolution in their policies,with both nations targeting high-centrality and competitive fields. While the United States demonstrates fluctuations in its restrictions,China maintains a consistent trajectory of support. This interplay shapes the global semiconductor industry and has far-reaching implications for technological development. Our findings highlight the importance of understanding these dynamics at the level of technological fields rather than industries. By doing so,we reveal the subtleties in how policies are formulated and their impacts on innovation. We believe this approach can provide valuable insights for policymakers and researchers alike. We invite future studies to build on our methodology and findings to deepen the understanding of technological nationalism and its role in shaping global technological landscapes.

China's automotive industry is undergoing a significant transformation towards electrification and intelligent mobility,emphasizing the need for effective marketing strategies to enhance brand perception and navigate the challenges in traditional sales channels.Within this evolving landscape,telesales play a crucial role in screening customer intentions,a process vital for boosting sales efficiency and reducing costs.However,the reliance on the subjective judgment of sales representatives in this multi-step process often leads to inefficiencies,including missed opportunities and resource wastage on low-intent customers,underscoring the need for a more precise approach in aligning marketing efforts with customer intentions in the automotive sector's dynamic environment.

Our research introduces an innovative model aimed at identifying customers' purchase intentions through the analysis of voice data from telesales conversations.Utilizing advanced voice analysis technology,this model scrutinizes various aspects of customer responses,including emotional,attitudinal,and cognitive dimensions,employing logistic regression techniques for accurate classification.The validation of this model involved an extensive dataset of 542 authentic customer voice recordings,which unveiled a significant link between factors such as emotional arousal,patience,and the likelihood of purchase intentions.Impressively,the model's precision,determined by AUC metrics in tests beyond the initial sample,surpassed the 92% mark.This level of accuracy underscores the model's effectiveness in pinpointing true purchasing intentions,setting it apart in the realm of customer intention analysis.The outcomes of our study underscore the model's practical value,especially in the context of telesales where distinguishing genuine buyers from a vast pool of contacts is crucial.By homing in on pivotal indicators like emotional arousal and patience,our model adeptly filters through the noise to identify those customers most inclined towards making a purchase.This capability not only streamlines marketing efforts but also significantly boosts sales efficiency by allocating resources more judiciously and increasing the focus on high-potential leads.Moreover,the real-world applicability of our model was further evidenced by its remarkable performance metrics,achieving a 90% success rate in capturing positive examples at a coverage rate of 23.1%.Such compelling results highlight the model's robustness and its suitability for broad implementation across the telesales operations within the automotive sector.The research not only paves the way for a more focused and effective marketing strategy but also heralds a new era in customer relationship management,where understanding and meeting the nuanced needs of potential buyers through sophisticated voice analysis becomes a cornerstone of success.

The automotive industry's evolution towards electrification,digitization,and sustainability marks a pivotal era demanding innovative marketing strategies.This model represents a significant advancement in utilizing voice analysis for customer intention identification,offering a more objective,scientific approach to understanding and catering to potential buyers' needs.Through this model,automotive companies can refine their telesales strategies,prioritizing high-intent customers,and thereby maximizing the efficiency of their marketing efforts.

This study explores how micro,small,and medium-sized enterprise (MSME) entrepreneurs respond to external crises and the psychological implications of their strategies. While prior research predominantly focuses on firm-level responses to crises,this study highlights the unique challenges faced by MSME entrepreneurs,whose personal resources,decision-making processes,and well-being are closely intertwined with their firms’ crisis responses. Understanding these dynamics is crucial given MSMEs’significant role in the global economy and their heightened vulnerability during crises. The research provides insights into how entrepreneurs make strategic decisions under pressure and how these decisions affect their mental health,filling an important gap in the literature on adversity and entrepreneurship.

Drawing on the Conservation of Resources (COR) theory,the study distinguishes two primary adaptation strategies:investment-oriented and conservation-oriented approaches. Investment-oriented adaptation involves actively investing resources,such as launching new initiatives or adopting innovative technologies,to mitigate the effects of the crisis and create value. Conservation-oriented adaptation,on the other hand,focuses on reducing costs and conserving existing resources,such as scaling down operations or cutting employee hours,to survive the crisis. The researchers hypothesized that resource availability,such as higher educational levels,innovation experience,and government relief benefits,would drive investment-oriented adaptation,while resource loss,particularly the adverse impacts of the crisis,would lead to conservation-oriented adaptation. Additionally,the study predicted that conservation-oriented strategies would be more strongly associated with anxiety than investment-oriented strategies,reflecting the greater psychological toll of managing resource depletion.

To test these hypotheses,the research employed two complementary studies. Study 1 used a time-lagged survey of Chinese MSME entrepreneurs during the COVID-19 pandemic,utilizing archival data collected before and after the outbreak. This study provided a detailed understanding of entrepreneurs’ responses within a single crisis context. Study 2 expanded on these findings by surveying entrepreneurs from multiple countries who faced various  crises,such as economic downturns and natural disasters. This broader approach allowed the researchers to examine the generalizability of their model across different cultural and economic contexts,enhancing the robustness of their findings.

The results revealed distinct predictors for the two adaptation strategies. Investment-oriented adaptation was positively influenced by resources such as innovation experience,higher educational levels,and government relief benefits. In contrast,conservation-oriented adaptation was driven by resource loss,particularly the adverse impacts of the crisis. These findings emphasize that the two strategies are governed by different mechanisms,underscoring the relevance of COR theory in understanding entrepreneurs’ responses to crises. Additionally,the findings suggest that multiple factors must be considered to fully explain why entrepreneurs adopt specific adaptation strategies during crises.

The study also investigated the impact of these strategies on entrepreneurial anxiety. Both investment-oriented and conservation-oriented adaptations were associated with heightened anxiety,but the psychological strain was more pronounced for conservation-oriented strategies. This aligns with the idea that focusing on resource depletion and cost-cutting may exacerbate stress,whereas investment-oriented approaches may provide a sense of purpose and control. These findings offer valuable insights into the mental health challenges confronted by entrepreneurs during crises and highlight the importance of considering psychological outcomes when evaluating adaptation strategies.

Interestingly,some predictors did not perform as expected. Entrepreneurial experience was not significantly related to investment-oriented adaptation in either study,potentially because external crises like the COVID-19 pandemic involve unprecedented challenges that render prior experience less relevant. Similarly,financial resources did not significantly predict investment-oriented adaptation,possibly because entrepreneurs with substantial financial resources may feel less urgency to act proactively. These non-significant findings highlight the need for future research to explore moderators and alternative explanations,as well as to consider the context-specific nature of crises.

The two studies demonstrated consistent findings for some variables,reinforcing the validity of the theoretical framework. Innovation experience and the adverse impact of crises emerged as robust predictors of adaptation strategies across different types and contexts of crises. However,there were also contextual variations. For instance,the effects of educational levels and government relief benefits on investment-oriented adaptation were significant in Study 1 but not in Study 2,suggesting that these relationships may depend on the type and context of the crisis. 

The study has important implications for theory and practice. Theoretically,it advances the literature on adversity and entrepreneurship by integrating COR theory to explain entrepreneurs’ crisis responses,providing a nuanced understanding of how resource availability and loss drive distinct adaptation strategies.Practically,the findings highlight the importance of resource accumulation during stable periods to enable investment-oriented responses in crises. Entrepreneurs should focus on continuous innovation and enhancing their educational qualifications or those of their decision-making teams to prepare for unforeseen challenges. Policymakers can play a crucial role by providing targeted relief benefits and fostering innovation-friendly environments to support entrepreneurs during crises. Moreover,addressing the mental health challenges associated with conservation-oriented strategies through community and governmental support programs can complement resource-focused interventions,ensuring a holistic approach to helping entrepreneurs and their firms navigate crises.

This paper investigates the influence of algorithmic decision-making on employees’ perceptions of fairness within the context of organizational management,particularly in light of the rapid advancements in artificial intelligence (AI).Leveraging the Stereotype Content Model (SCM),the study explores differential fairness perceptions between algorithmic and human decision-makers,particularly in adverse outcome scenarios.Findings suggest that algorithms are generally viewed as fairer than human managers,with perceptions influenced significantly by the type of task being evaluated.

As AI technologies continue to permeate various business operations,organizations increasingly deploy algorithms for diverse managerial functions,including human resources management,task allocation,and performance evaluations.This shift raises critical questions about how employees perceive the fairness of decisions made by algorithms as opposed to human managers.Given that fairness perceptions are pivotal to employee satisfaction,organizational commitment,and performance,understanding these dynamics is essential for effective organizational management.

Existing research on algorithmic decision-making offers mixed insights into its impact on perceived fairness.Some studies argue that algorithms,by relying on data and objective models,minimize human biases and enhance fairness.Others,however,highlight potential shortcomings such as neglect of qualitative information and lack of transparency,which can lead to perceived unfairness.Additionally,the literature suggests a gap in understanding the effects of decision outcomes on fairness perceptions,particularly when outcomes are unfavorable,thus forming the basis of this study’s inquiry.

This study employs scenario-based experiments to examine how employees perceive fairness when confronted with unfavorable decisions executed by either algorithms or human managers.These experiments are designed to cover a range of task subjectivities,from highly objective,data-driven tasks to those requiring significant human judgment and intuition.

The experimental results reveal that decision-maker type and task nature significantly affect fairness perceptions.In scenarios involving objective tasks,algorithms are perceived as fairer due to their presumed impartiality and lack of human error.For subjective tasks,algorithms are still viewed more favorably,but this is attributed to human managers being perceived as potentially indifferent or lacking empathy.This dichotomy underscores the complexity of fairness perceptions and suggests that while algorithms may excel in objectivity,they may fall short in areas requiring emotional intelligence.

This research adds depth to the discussion on AI’s role in management by delineating how task type and outcome influence fairness perceptions differently under algorithmic versus human decision-making.It offers insights that could help organizations better integrate AI into their management practices,ensuring that fairness perceptions are carefully managed to maintain employee satisfaction and performance.

Future researches could broaden the investigation into other organizational contexts and include longitudinal studies to assess how fairness perceptions evolve with long-term exposure to algorithmic decision-making.Moreover,further studies could explore how increased transparency and employee involvement in algorithm development might enhance trust and fairness perceptions,fostering a more equitable organizational environment.

Faced with pressures such as declining sales caused by the weak automobile market,increasing customer acquisition costs caused by changes in customer structure,and scarcity of offline traffic,traditional car companies urgently need to carry out digital reforms,and car live stream has gradually become an important way for car manufacturers to attract traffic.In order to accurately capture sales leads and analyze product feedback,the live streaming platforms and vehicle sales teams need to correspond the current live vehicle type with the feedback and live data of each live audience.However,in practical business,many live broadcasts only indicate the brand without identifying the current live vehicle type,or a live broadcast will broadcast multiple types of the same brand.It is impossible to directly obtain the currently live vehicle type and then match the data for further analysis.

This paper finds these practical business issues and innovatively proposes a business scenario for identifying the live vehicle type to effectively solve the pain point of the live streaming platforms and vehicle sales teams.In the process of live streaming,the vehicle type is usually displayed at the license plate position.This is a strong signal scene in a high-resolution image,that is,the signal occupies a small size in the whole picture,but it largely determines the classification of the picture.Moreover,the number of targets in the image has been clarified in advance,and the scene is relatively simple and targeted.However,traditional image processing methods are relatively complex and not effective for such scenes.This paper draws on the target retrieval method and proposes a relatively simple image classification model for scenarios with limited annotation data and strong signal scenes.It applies transfer learning to solve the problem of few annotation samples and limited resources and creates a boundary box regression based on the IoUB Loss function to more accurately determine the license plate area.Specifically,the model is a two-stage model.The first step is to scan the image,train a binary classification model through transfer learning based on the VGG16 network and use the boundary box regression based on the  IoUB Loss function to predict the license plate position.The second step trains a classification model through transfer learning based on the VGG16 network to classify the predicted license plate area.Finally,the model is applied to the live vehicle type identification scene.

By training the vehicle images during the live stream of Audi cars on the TikTok,the prediction accuracy of this model in the test set images reaches 47.4%.Considering that the innovation of this paper does not focus on creating a new high-precision image recognition method,but on identifying live vehicle type to effectively solve the business issue of live streaming platforms and vehicle sales teams,this model is compared with the traditional image processing model and classic object detection model (Faster RCNN).It is found that：① traditional image processing models are not suitable for such strong signal scenarios.Dimensionality reduction will lose signal information and lead to significant performance degradation,while the model in the paper has a certain optimization effect;② In the second step,using the predicted license plate area image for training is more effective than directly training the annotation frame;③ In the case of limited annotation data,the relatively simple model proposed in the paper has higher prediction accuracy than Faster RCNN model and has a certain practical effect.It can help live streaming platforms and vehicle sales teams handle live vehicle identification problems in their business more simply and efficiently.At the same time,the model in the paper is also suitable for other application scenarios with high-resolution,limited annotation data,and strong signal image classification problems.Users can migrate and apply the model according to actual scenarios.

Motivated by the absence of personal bankruptcy law in China,this paper incorporates non-defaultable personal debt and defaultable bank debt into the capital structure choice of a firm.By constructing a dynamic stochastic general equilibrium model,we investigate the macroeconomic implications of personal bankruptcy.In the model,when deciding over debt structure,entrepreneurs must consider a tradeoff between default losses associated with bank debt and unlimited liability associated with personal debt.Personal bankruptcy attaches default losses to personal debt but at the same time alleviates the burden entrepreneurs bear because of the unlimited liability.Our counterfactual analyses show that the positive side dominates.Personal bankruptcy is helpful in expanding economic activities,reducing banking sector instability,alleviating business cycles,and improving welfare.

To elucidate the intrinsic logic behind personal bankruptcy,corporate defaults,and asset structures,we incorporate the endogenous choice between informal personal lending and formal bank lending by entrepreneurs into a Dynamic Stochastic General Equilibrium (DSGE) model within the current institutional setting of China.Additionally,we qualitatively discuss and quantitatively analyze the macroeconomic effects of the personal bankruptcy system.Specifically,we begin by employing a two-period static model that simultaneously considers informal and formal lending.This theoretical framework allows us to delve into the trade-offs faced by entrepreneurs when selecting different debt structures and understand the dual nature of the personal bankruptcy system.Subsequently,we integrate equity financing to create a comprehensive capital structure within the DSGE model.The results from stochastic simulations reveal that entrepreneurs dynamically adjust their asset structures to address productivity shocks within the system.Furthermore,in our counterfactual analysis,we introduce provisions of the personal bankruptcy system,and the quantitative findings underscore the predominant positive impact of this institutional reform.It is shown to be conducive to expanding the scale of macroeconomic activities and direct financing,reducing corporate leverage and bank loan default rates,and ultimately enhancing societal welfare.

This paper contributes significantly in several ways:Firstly,it incorporates the trade-off between bank and informal lending into corporate financing decisions,aligning more closely with real-world economic dynamics.It holds substantial policy implications for the advancement of personal bankruptcy legislation in China and guides market participants to rationally assess the economic impacts of such a system.Secondly,within the unique backdrop of China where personal bankruptcy is not allowed in informal lending,this paper pioneers the construction of a DSGE model to comprehensively analyze and quantitatively solve the macroeconomic effects of the personal bankruptcy system from perspectives such as economic growth,social financing structure,and societal welfare.Thirdly,on the policy front,our findings suggest that implementing a personal bankruptcy system can effectively alleviate the challenges of direct financing for small and medium-sized enterprises in China and should be pursued expeditiously.Additionally,complementary measures,including enhanced regulatory frameworks,should be implemented to mitigate potential risks like “pseudo-bankruptcies” and increased non-performing assets.

The paper is structured as follows: In the first section,we introduce the background of personal bankruptcy law,explain the theoretical trade off of this policy and conduct literature review.In the second section,we construct two static models based on the trade-off between bank and informal lending and theoretically analyze the logical relationship between corporate debt structure and bankruptcy mechanisms,along with the potential economic effects of personal bankruptcy systems.In the third section,we build,solve,and simulate a DSGE model that incorporates endogenous choices between bank and informal lending,quantitatively analyzing entrepreneurs' trade-offs in response to productivity shocks.In the fourth section,we introduce the personal bankruptcy system in a counterfactual model and conduct numerical analyses,examining the impact and mechanisms of different degrees of personal bankruptcy systems on the macroeconomy.Finally,in the fifth section,we summarize the entire paper and present policy recommendations.

The past decade has witnessed a rapid growth of money supply in China and consequently a large amount of money has flown into the real estate market leading to a real estate bubble,and shifting the economy from real to virtual.This has raised special attention from policy makersto professional economists and also has ignited heated debate on the direction of monetary flows and the efficacy of monetary policy.

Following Brunnermeier and Sannikov (2016),we construct a stochastic growth model with endogenous asset portfolio choice.In the model,each household owns productive capital and is subject to idiosyncratic shocks.Constrained by financial frictions,households are unable to insure against such idiosyncratic risks in full,so they need to do diversification through careful asset allocation.Money is risk-free and functions as a store of value,as illustrated in the conventional literature.Our innovation relative to the previous work is to include a (risky) bubbly capital for the purpose of examining the dynamic structural change of the real sector and virtual economy.The bubbly capital does not create real wealth but gives its holder“money illusions”.The return rate for bubbly capital is monotonically increasing in its social average investment rate.That is,the more household purchase the bubbly capital,the higher its return.

The effect of money growth on the economy has two competing forces.The first is the aggregate effect,i.e.,an increase in the money growth rate leads households to hold more non-money assets,thus pushes up the aggregatesavings,accumulates both productive and bubbly capital and finally promotes economic growth.The second is called the structural effect,i.e.,an increase in the money growth rate reduces the proportion of productive capital relative to bubbly capital via household portfolio choice and therefore worsens the phenomenon of“making the economy shift from real to virtual”and finally slows down economic growth.Risk is an important factor to determine the relative magnitude between these two effects.When the bubbly capital is very risky (more volatile),the aggregate effect looms large and increasing the money growth rate may boost economic growth.When the bubbly capital is not very risky,the structural effect becomes the dominant force.In this case,investing in the bubbly capital is more profitable and inflation can only transform more capital to become bubbly rather than productive and eventually hinders economic growth.Further policy simulation results show that the combination of macro-prudential policy tools and monetary policy can prevent the economy from shifting to virtual,as well as maximize social welfare.More precisely,①when the volatility of bubbly capital is sufficiently high and exceeds some bound,there exists an optimal money growth rate maximizing the social welfare; ②as long as the volatility of the bubbly capital is below this bound,increasing money supply results in welfare loss.We use the Chinese housing market as a laboratory to conduct experiments.We find that taxing on the real estate could level up the effectiveness of monetary policy through cutting down the return on holding real estate and thus suppressing the structural effect of increasing money growth.Further welfare analysis illustrates that there may exist a combination of positive real estate tax rate and positive money growth rate which maximize the total social welfare.

Our paper makes contributions in several aspects.Firstly,the paper analyzes the reason and the underlying mechanism behind the phenomenon of“making the economy to shift from real to virtual”from the perspective of risk.We find from the comparative statistics that the risk of bubbly capital is key to understanding this phenomenon.The existing literature only considers the return rate rather than the risk of investment.Secondly,the paper embeds portfolio choice into an economic growth model and highlights the dual effects of money growth rate on economic growth,i.e.,the aggregate effect and the structural effect.The existing literature mainly considers a single effect.For example,Brunnermeier and Sannikov（2016）only emphasize the aggregate effect by showing that an increase in the money growth rate could decrease the riskless return rate,thus leading households to be exposed more to risky investment and promoting economic growth.However,after introducing another bubbly capital into the economy,we find that increasing money growth rate would change the Sharpe ratio of productive and bubbly capital and generate a structural effect,which pushing the economy to shift from real to virtual from bad to worse.Thirdly,the paper constructs aneconomic growth model with portfolio choice to offer a unified framework for exploring the effects of the combination of macro-prudential policy tools and monetary policy from a long-run growth perspective.The existing papers analyzing the macro-prudential policy are mainly based on the DSGE framework,so they are unable to discuss either the growth issue or the asset allocation problem.The framework in this paper can examine the interactions between portfolio choice,optimal monetary policy and macro-prudential policy.

The key to sustainable growth in banking lies in the Communist Party of China's (CPC) leadership and the refinement of internal governance mechanisms.Effectively preventing financial risks will significantly propel the banking sector towards high-quality development.Hence,it is crucial to actively explore how organizations of the CPC can engage in the governance structure of banks,and establish a governance model tailored to China's unique characteristics.Commercial banks face substantial information asymmetry and high regulatory costs,making refined internal governance mechanisms pivotal for risk management within these institutions.As the statutory procedures and mechanisms for CPC committee members to join corporate boards and senior management continue to improve,a “two-way entry and cross-serving” model emerges,forming a dual governance framework that blends “CPC-led governance” with “modern corporate governance”.

This paper summarizes the involvement of CPC organizations in the governance of 215 commercial banks from 2002 to 2020.In the context of economic policy uncertainty,this paper examines how the integration of CPC into governance through “two-way entry and cross-serving” affects risk prevention in banks.The findings suggest:Firstly,CPC's involvement significantly reduces banks' risk exposure by mitigating the adverse effects of economic policy uncertainty.Particularly,when the CPC committee's Secretary concurrently serves as the Chairman,this combination has a more pronounced impact on reducing bank risk compared to CPC committee members in supervisory and managerial roles.Secondly,CPC's “Three Importance and One Greatness” system,covering major issues,important appointments and removals,major projects,and the use of large amounts of money,displays proactive risk prevention features.Thus,during increased economic policy uncertainty,banks with CPC involvement in governance mitigate operational volatility,reducing risk exposure.This is evident in banks avoiding excessive liquidity hoarding and diversifying loans to decrease concentration,enhancing their capacity to serve entities.Thirdly,even after accounting for inherent influences by organizing and assessing martyrs' cemeteries and the number of martyrs in the banks' regions,the results remain significant.

The above results demonstrate that integrating CPC with bank governance effectively enhances the capability to serve financial entities.Continuing to refine the “two-way entry and cross-serving” leadership model proves crucial in bolstering risk prevention in the banking sector.It also adeptly counters the adverse impacts of rising global economic policy uncertainties on the financial system.

Therefore,this paper provides China's experience in preventing and resolving significant financial risks by integrating CPC with bank governance.In contrast to Western traditional corporate governance studies,this article combines China's institutional system,cultural background,and the centralized leadership of CPC in economic affairs to demonstrate that CPC involvement in bank governance aligns with the interests of the CPC and the people.Simultaneously,it effectively reduces principal-agent conflicts.Combining data and empirical analysis,the dual governance system integrating Party with corporate governance is scientifically and significantly essential for refining internal governance mechanisms within banks.It offers a new research perspective on bank governance and risk control.

CPC organizations embody a collectivist culture.Through the principle of “the Party assuming the responsibility for cardres' affairs”,these organizations can engage more deeply in supervising and managing processes within banks and even the entire financial sector,aligning well with China's reality.In the future,exploring the specific pathways of CPC organization involvement in governance and finding more scientific means to leverage this role will be crucial for better utilizing this unique system and enhancing internal governance mechanisms.

Innovation is a central part of economic growth.Existing theories elaborate on the causes and consequences of firm innovation,but less discuss the determination of the direction of firm innovation.How innovation decisions are made and whether the direction of innovation is steered are questions well worth answering.There is often a lack of incentives in the market for the production of high-cost,low-polluting technologies.Demand for cleaner technologies is always low.This is an important manifestation of market failure.Firms prefer the low-cost but highly polluting production technologies to the costly green production technologies.Relying on market forces alone cannot force enterprises to abandon highly polluting production technologies and switch to cleaner technologies.Strict environmental protection policies will help to reverse this market failure.China has gradually introduced four batches of inspections since July 2016,with each batch lasting one month,which is named Central Environmental Protection Inspectorate (CEPI).The policy not only raises the penalties for polluting behavior of enterprises in the system development,but also realistically supervises enterprise pollution,which effectively reduced pollution.The CEPI will have a twofold impact on enterprises’ innovation.One is to increase the relative cost of using polluting technologies for enterprises,forcing them to innovate in a green way.The second is to increase R&D investment in cleaner technologies,crowding out the R&D resources that firms invest in other technologies.This paper explored how the environmental regulation policy affects the direction of firms’ technological innovation,and found that the central environmental inspection policy changes firms’ innovation decisions.Firms always react to the change of costs and benefits.Under the strict environmental regulation policy,firms re-examined the benefits of adopting different technologies.That increased the demand for cleaner technologies,which spurred the effect of promoting innovation shifts.This paper firstly demonstrated the promotional effect of the environmental policy on the “Green Patents” with General DID.This paper secondly set other “non-green patent” that are not related to environmental protection as a control group.Environmental policy reversed the low demand for cleaner technologies in the market.The applications for “non-green patent” increased by 2.74% after the CEPI,while “Green Patents” increased by 3.39% more.Strict policies can reverse the problem of insufficient demand for clean technologies and inhibit the short-sighted behavior of enterprises blindly pursuing low-cost technologies.

“Green Patents” had an overall lower output size than other “non-green patents” before the policy intervention,and this trend remained stable over the 21-month period before the policy intervention.After the policy intervention of the CEPI,this difference has changed significantly.The portion of “green patents” that was originally lower was gradually filled in,and the difference between the two gradually disappeared.In the long term,the effect of the central environmental protection inspections will remain in place until 2021.This suggests that short-term policy shocks can have a long-term impact after a period of effective implementation.

At the same time,we also found that the shift in technological innovation brought about by environmental policies comes at a cost.In this paper,based on the semantic analysis of patent applications,the patents were categorized into six groups according to the remoteness of semantic similarity:95%~100%,75%~95%,50%~75%,25%~50%,5%~25%,and 0%~5%.The results showed that enterprises have adjusted their R&D allocation to meet the requirements of environmental protection inspections,squeezing out some of the R&D of other types of patents.More resources are allocated to environmental protection-related technologies,showing the “crowding out effect”.The cost of environmental protection inspections to reduce pollution is to force some patents to be replaced by cleaner technologies.

This paper also did a comparative analysis using the IPC classification.The main conclusion that policy promotes innovation steering still holds when the IPC classification is used.However,in comparison,the innovation steering effect produced by policy is significantly higher when measured based on the text analysis method.The conclusions drawn from the method based on textual analysis were more reliable.Because some of the technologies of synergistic pollution reduction are not counted when counting green patents with the IPC classification method.In addition,applicants have a tendency to categorize technologies when applying for patents.Artificial categorization will bring about greater errors and deviate from the problem that the technology is actually intended to solve.

This paper supplemented the robustness test on the definition method and observation window.Changing the way of defining green patents does not change the main conclusions.Narrowing the observation window to 2015 to 2018,the central environmental protection inspection policy can still lead to faster growth of green invention patents.In both the short and long term,the CEPI had led to a change in the cost-benefit relationship of technology,triggering a reorientation of corporate innovation.

This paper adds new evidence to the benefits of environmental regulation and expects to inform policymakers’ decisions.This paper employs textual analysis to provide a new perspective for research related to innovation,hoping to avoid the deception and arbitrariness in the application of scientific and technological achievements.That will improve the accuracy and efficiency of the research on China’s innovation output.

With the booming development of China's mutual fund market,mutual funds have been sought after by a large number of investors.At the same time,Fund of Funds (FOF) has also experienced explosive growth.How to select funds that can consistently beat the market from among the many funds has attracted widespread attention from academia to practitioners.On the one hand,facing with many fund options,individual investors hope to choose funds with good performance to achieve higher investment returns in the future; On the other hand,FOF  managers are also eager to select excellent funds to improve their performance to attract new capital inflows,expand the management scale of FOF  and achieve higher returns.However,many studies have shown that there is a clear phenomenon of common stockholding among China's mutual funds.Different funds form a complex relationship network through common stockholding,called a “fund network”.Since the stockholding characteristics between connected funds are relatively similar,their returns tend to be more similar,which increases the difficulty of mutual fund selections.Based on this,this paper focuses on how the fund network affects fund performance and explores how to select funds with real stock-picking ability.

There are two main issues with existing literature on the performance of funds.Firstly,when studying the impact of fund networks on fund performance,most papers only use the topology of the fund network as an explanatory variable,with fund performance being the explained variable.Few papers directly incorporate the fund network matrix into model estimation,which results in a loss of connection information between funds.Secondly,the current research on choosing funds only takes into account the “luck”,and does not consider the network connections between funds.However,a large amount of existing research shows that fund networks do affect fund performance.Therefore,the current fund selection method is not conducive to investors or FOF managers in selecting funds.

This paper proposes a mutual fund selection model and its estimation method under network adjustment.The paper innovatively utilizes the fund's heavy stock network matrix to construct a penalty term,which is introduced into the objective function of parameter estimation to restrict the difference in fund alpha(α) between two connected funds,thereby estimating the fund's performance alpha after network adjustment.The paper also theoretically proves the proposed method can effectively control the False Discovery Rate (FDR) under certain conditions.In the empirical study,the paper uses balanced panel data of 596 open-ended funds in China from 2016 to 2020 to explore the impact of fund networks on fund performance and proposes a network-adjusted fund selection strategy based on the FDR method.This paper found that:Firstly,the holdings of China's funds in the past five years had a high degree of overlap,resulting in a high correlation between fund performances.Secondly,the findings of this paper verified the well-known “Matthew effect” phenomenon in the mutual fund market.In terms of the fund market,the fund network would have a positive impact on positive alpha funds,enabling them to achieve better investment performance; on the contrary,it would have a negative impact on negative alpha funds,resulting in lower investment returns.Thirdly,the network-adjusted fund selection strategy proposed in this paper performed well and stably compared with different fund selection portfolios under different holding periods.This indicates that separating the fund network effect from fund performance can help select funds with real stock-picking ability,which can bring sustained investment returns to investors.

Based on the above conclusions,this article proposes the following suggestions:Firstly,if fund managers want to achieve absolute performance advantages,they should first improve their stock-picking ability,and then rely on the common stockholding phenomenon in the fund market to learn the positions of other funds with stock-picking ability,so as to achieve the “icing on the cake” effect.Secondly,investors or FOF  managers can use network-adjusted fund selection strategies to eliminate the impact of fund networks on fund performance and select excellent funds.

Finally,some feasible research directions for future work are proposed.Firstly,it is worth expanding our method to encompass situations where funds are linked indirectly,as we seek to examine the effects of indirect common stockholding on fund performance.Secondly,it is of interest to construct a directed dynamic fund network based on the change of fund holdings and explore its dynamic impact on fund performance.Thirdly,the sample period presents unbalanced fund data as a result of liquidation or new establishment.Therefore,it is important to expand the conventional approach to fund selection,which is based on balanced panel data,to include fund selection under unbalanced panel data.Fourthly,since private equity funds can choose to disclose performance autonomously and have strict liquidation mechanisms,their data usually have serious missing features.Extending the method of this paper to private equity fund selection under missing data also has practical significance.

How organizations adapt to rapidly changing and dynamic environments through continuous learning is considered central to organizational learning theory.Organizational learning has been driven by human experience and social interaction,facilitating knowledge creation,transfer,and application.However,the introduction of artificial intelligence (AI) is redefining the premises and mechanisms of learning,influencing traditional theoretical frameworks,and introducing complexities in multi-level interactions.These changes are primarily reflected in three areas:the expansion of learning actors,the restructuring of learning mechanisms,and the governance of learning outcomes.Although human-machine interactive learning is reshaping the paradigm of organizational learning,there is still a lack of a systematic understanding of the evolution of organizational learning research under the impact of AI.Thus,this study aims to comprehensively review the evolution of organizational learning research in the context of AI.

Specifically,this study conducts a systematic review of 74 key articles on organizational learning in the context of AI.Using the “individual-group-organization” framework of organizational learning as its foundation,the study conducts a review across four key themes:① individual-level learning in the AI context; ②group-level learning in the AI context; ③organizational-level learning in the AI context; ④ multi-level interactive learning in the AI context.Individual-level learning focuses on how a single individual modifies their cognitive structures and behavioral patterns through intuition and interpretation,with its core centered on knowledge construction at the individual level.Group-level learning refers to the process in which multiple individuals engage in collaborative learning within a group,emphasizing the interpretation and integration of individual knowledge within the team.Organizational-level learning concerns the integration and institutionalization processes across teams,focusing on how knowledge extends from the team level to the entire organization.Multi-level interactive learning examines the flow and interaction of knowledge across individual,group,and organizational levels,emphasizing how feed-forward and feedback processes shape learning at different levels.

The findings reveal several critical points.Specifically,at the individual level,AI enhances intuition,supports interpretive reasoning,and promotes action reflection,improving decision-making and adaptability in complex environments.However,it may also lead to negative consequences such as over-reliance on technology and a decline in autonomous learning capabilities.At the group level,AI facilitates the building of cognitive consensus,enhances collaboration efficiency,and improves decision-making effectiveness,but it may also reduce the diversity of knowledge within the group.At the organizational level,AI restructures digital memory mechanisms,optimizes knowledge management,and drives cross-boundary learning through data,enhancing organizational adaptability and stakeholder engagement,while also introducing potential ethical risks.In terms of multi-level interactive learning,AI accelerates the speed and breadth of knowledge flow both internally and externally,breaking down the knowledge barriers between organizations and ecosystems,and making multi-level interactive learning within organizations more dynamic,complex,and competitive.Building on this,this study constructs a research framework for organizational learning in the context of artificial intelligence,revealing the systematic evolution of organizational learning research under the impact of AI.First,AI is identified as a catalyst for changes in learning contexts,driving individuals,groups,and organizations to adopt integration pathways.Second,the integration of AI is observed to reshape adaptive actions in human-AI collaboration,with these actions influenced by technological,organizational,and environmental factors.Finally,the outcomes of these adaptive actions reinforce organizational systems through feedback mechanisms,enabling coevolution between AI and organizational systems,driving firm’s adaptive innovation and growth,and  forming a self-reinforcing cycle of coordinated evolution.

This study identifies research gaps and proposes four future research directions:①analyzing the multidimensional interactions of individual learning in the AI context,focusing on complex tasks,cross-boundary integration,and long-term effects.For example,the cognitive reshaping mechanism of AI in key managerial learning,the potential exploration of AI in individual cross-domain knowledge integration,and the negative effects of long-term interaction with AI on individual learning.②Deconstructing the psychological processes of group learning in the AI context,including trust building,role perception,and collaboration structures.For example,the trust mechanism of shared cognition in teams under AI contexts,AI-driven role cognition and dynamic task allocation models in teams,and the impact of team structure optimization on fostering diverse perspectives and collective intelligence.③Uncovering the dynamic adjustments of organizational learning in the AI context,such as memory updates,knowledge integration,and contextual adaptation.For example,the systematic management of organizational digital memory in AI contexts,the integration mechanism of AI knowledge and human knowledge within organizations,and the multi-context adaptation model of data-driven learning.④Constructing the co-evolution mechanism between AI capabilities and multi-level learning,emphasizing influence pathways,capability emergence,and governance frameworks.For example,the influence pathways of multilevel organizational learning on AI capabilities,the emergent capabilities and strategic consequences of AI in interaction with multilevel learning,and the critical role of AI governance in the co-evolution process.

This study has three contributions:First,it reveals the transformations in organizational learning under the AI context,encompassing changes in learning contexts,processes,and outcomes.These include the disruptive impact of AI on learning contexts,the expansion of feedforward and feedback mechanisms,and the adaptive evolution of human-AI collaboration,thereby extending the applicability and developmental scope of organizational learning theory in the AI era.Second,it systematically reviews and synthesizes the core themes and limitations of existing research on organizational learning in the AI context,constructing a comprehensive research foundation and theoretical framework.Third,it addresses the major gaps in current studies by proposing future research directions,offering clear pathways and guidance for advancing organizational learning research in the AI era.

On September 22,2020,President Xi Jinping announced at the 75th Session of the United Nations General Assembly that China would strive to peak CO2 emissions before 2030,and achieve carbon neutrality before 2060.China is taking pragmatic actions towards these goals.As a responsible country,China is committed to building a global climate governance system that is fair,rational,cooperative and beneficial to all,and makes its due contribution to tackling climate change using its greatest strengths and most effective solutions.

China's energy conservation and emission reduction policy system originated in the 1980s.With transition from a planned economy to a market economy,China's energy-saving and emission reduction policies have gradually transitioned from administrative directives to market-oriented economic incentives,accumulating rich experience in reducing carbon emissionsduring the past 40 years.The establishment of the dualcarbon goals in 2020 provides clearer goals and greater challenges for China's emission reduction undertakings.

Carbon pricing mechanism is an important market-based policy option adopted by many countries and regions to address climate issues,and it mainly includes two methods：carbon tax and carbon emissions trading.Carbon emissions trading is currently the core market regulation mechanism in China.China proceeded carbon emissions trading pilots in seven provinces and cities in 2013,and the carbon trading market was officially launched in July 2021.It covered more than 4 billion tons of emissions,becoming the world's largest carbon trading market once the market went online.Carbon trading is playing an important role in the process of China's emission reduction,but its effect is limited.

Carbon tax,another representative market incentive policy tool,is frequently advocated as a cost-effective instrumentin reducing emissions,so it might be necessary to be introduced to China in due time to make up for the shortcomings of current carbon trading policies.Many countries or regions have successfully achieved the synergistic development of carbon tax and carbon trading.Therefore,the feasibility of levying a carbon tax in China and how to realize the coordinated development of carbon tax and carbon market are hot topics of research.

Studies on China's carbon tax system mainly focus on the feasibility analysis of introducing a carbon tax and using large-scale macro models such as “Computable General Equilibrium” (CGE) to simulate the impact of carbon tax on emission reduction and economics.Especially after the launch of the carbon emissions trading pilot in 2013,domestic research on the carbon tax has been concerned more about the design of the carbon tax mechanism,and the coordinated development of carbon tax and carbon trading,while research on quantitative analysis has stagnated.Earlier quantitative research on China's carbon tax system based on CGE models are complicated,with high computational costs,and it is difficult to clarify the policy transmission mechanism.

Imposing carbon tax would have macroeconomic and environmental impacts.The economy acts as a complex network that closely connects the production sector with the consumption sector.The production network contains rich information about the structure and interactions of industries.Acemoglu et al.(2012) find that the production network is an important channel for the propagation of sectoral individualshocks into macroshocks.Microeconomic idiosyncratic shocks may lead to aggregate fluctuationsin the presence of intersectoral input-output linkages.Therefore,when formulating or evaluating policies,it is not comprehensive enough to consider the direct impact alone,but necessary to take the aggregate effects caused by the production network into account.The propagation mechanism proposed by Acemoglu et al.(2012) also applies to the carbon tax.King et al.(2019) found that sector-specific carbon tax changes can have complex general equilibrium effects in the presence of intersectoral linkages.They provide an analytical characterization of how incremental taxes on emissions of any set of sectors impact aggregate emissions,thereby offering a novel perspective for the analysis of carbon taxes.

In this paper,we focus on theeffects of carbon tax in China from the perspective of production networks.We first calculate sectoral carbon emissions generated from energy consumption in 2020 and estimate the embodied carbon flow matrix based on Chinese 42-sector and 153-sector input-output tables.Then we simulate how an incremental sector-specific carbon tax influences the economys total carbon emission through the intersectoral production network linkage as well as the impact on the labor input,output and carbon emission of the taxed sector drawing on the model constructed by King et al.(2019).We find that,due to the existence of production networks,the imposition of a carbon tax will not only reduce the total carbon emissions of the economy by decreasing the output of the taxed sector,but also trigger indirect effects through the upstream and downstream linkages.Therefore,targeting sectors based on their position within the production network can achieve a greater reduction in aggregate emissions than taxing sectors solely based on their direct emissions.

The simulation results show that the sectoral ranking of the carbon reduction effect and direct emissions does not correspond one-to-one.Taxing “Petroleum,Coking Products and Processed Nuclear Fuel Products”, “Production and Supply of Electricity and Heat”, and “Metal Smelting and Rolling Processed Products” among the 42 sectorsor taxing  “Production and Supply of Electricity and Heat”, “Refined Petroleum and Processed Nuclear Fuel Products” and  “Rolled Steel Products” among the 153 sectors will bring the most significantcarbon reduction effect.As the above sectors are the main suppliers of raw materials and major carbon emitters in the production network,imposing carbon tax will prompt them to reform their production technologies and optimize their energy structures,thereby achieving an effective reduction in the total carbon emissions of the economy through their upstream and downstream influences.In addition,taxing on “Petroleum,Coking Products and Processed Nuclear Fuel Products” can bring the biggest decrease in total carbon emissions with a smaller drop of its own production.

Relational structures consisting of different types of interactions among several groups of entities are very common nowadays.As a useful tool for analyzing this type of data,multi-layer networks have gained increasing attention in recent years due to their ability to capture the complexity of real-world systems.Multi-layer academic networks are a specific type of multi-layer network that consists of multiple layers of relationships among academic entities,such as researchers,institutions,papers,or journals.Typical examples of multi-layer academic networks include the collaboration network that represents co-authorship relationships among researchers,the citation network that represents citation relationships among papers,and the journal citation networks that represent citation relationships among journals.They have been used for various purposes,such as identifying research areas,evaluating research impact,predicting scientific trends,studying the diffusion of scientific knowledge,and supporting science policy and decision-making.Overall,multi-layer academic networks provide a powerful tool for understanding and analyzing the complex relationships that underlie academic communities and their impact on scientific knowledge production and dissemination.

In this work,we collect data from 42 statistical journals published between 1981 and 2021 from the Web of Science(www.webofscience.com).Our LMANStat dataset includes basic information on 97,436 papers,including their title,abstract,keywords,publisher,published date,volume and pages,document type,citation counts,author information (name,ORCID,address,region,and institution),as well as their reference lists.Based on this information,we construct multi-layer academic networks,including collaboration network,co-institution network,citation network,co-citation network,journal citation network,author citation network,author-paper network,and keyword co-occurrence network.These networks change dynamically over time,providing a dynamic analytical perspective during analysis.Moreover,we also include rich nodal attributes of authors,such as the authors’ research interests,to enhance the usefulness of our dataset.The LMANStat dataset is publicly available on GitHub,and can be accessed directly at https://github.com/Gaotianchen97/LMANStat.

We present a comprehensive overview of our methodology,which covers the complete workflow from data collection to data cleaning,as well as the construction of multi-layer academic networks.Subsequently,we provide detailed explanations regarding author and paper identification,the extraction of author attributes,and the construction of multi-layer academic networks.Next,we validate the dataset through various potential scenarios for exploring and analyzing our multi-layer academic networks.To emphasize the usability of our dataset,key insights into the characteristics of the data are also provided,aligning with historical research findings and the consensus among statisticians.More importantly,the LMANStat dataset is extensively utilized by our research team to validate its usability.In our multi-layer academic networks,the collaboration network and citation network are the most commonly used networks.Therefore,we utilize them for verification.Additionally,we also consider the journal citation network with journals as nodes and the keyword co-occurrence network with keywords as nodes to validate the LMANStat dataset.

For the collaboration network,the scale-free phenomenon can be detected through a log-log degree distribution plot,which is also referenced in research on collaborative networks.The average number of authors per paper shows an increasing trend by year,indicating collaboration trends in statistical research.By visualizing a sub-network of the collaboration network,we identified the top 4 authors with the highest degrees,whose innovative methods and insights have had a profound impact on the field of statistics.As for the citation network,the in-degree of a paper is crucial as it represents the number of times the paper has been cited within the network.A higher in-degree implies a greater number of citations for a paper.Within our dataset,the average in-degree per paper is 5.31,which correlates closely with the Impact Factor (IF) of the selected journals.

In addition,journal citation networks are often employed for ranking journals,which is considered an important indicator for evaluating the quality and impact of publications in specific research fields.Therefore,we validate the accessibility of the journal citation network through journal ranking.By calculating the PageRank centrality of each node (journal),we can effectively rank journals based on their importance.Interestingly,we observe the phenomenon that the ranking of journals based on PageRank centrality closely aligns with the expectations and intuitions of statisticians.This suggests that the PageRank-based approach provides a ranking that resonates well with the perceptions of experts in the field.

It is important to note that the multi-layer academic networks presented in this paper are all dynamic in nature.Taking the citation network as an illustration,we showcase the dynamic nature of the network.The visualization includes snapshots of the network from different time periods:1980—2006,1980—2010,and 1980—2020.It is evident from the visualization that the citation network exhibits a community structure that undergoes constant changes over time.This community has shown continuous growth over the years,as indicated by the increasing number of papers associated with variable selection.In conclusion,it is worth emphasizing that the networks within this dataset are all dynamic,thereby enabling the exploration of dynamic nature.

In conclusion,the paper utilizes statistical publication data collected from the Web of Science to provide a large-scale,high-quality multi-layer academic network dataset (LMANStat dataset).The study further validates the quality and usability of these constructed multi-layer academic networks from multiple perspectives.It discusses feasible research directions and application scenarios,including but not limited to exploring community structures within academic networks,tracing the development and evolution of research topics,investigating mechanisms behind citation counts of papers,discussing the impact of international and inter-institutional collaborations,exploring career planning and development of researchers,and establishing more diversified journal ranking systems.

This paper develops a novel intra-urban spatial structure model that extends traditional linear or circular city frameworks to accommodate cities of arbitrary shapes.For the first time,it examines how the number and location of urban sub-centers—key components of a polycentric urban layout—affect residents’ welfare by reshaping the spatial structure within cities.The theoretical model assumes that the number and locations of sub-centers are exogenous variables determined by urban planning as well as historical and geographical factors.Given the locations of these sub-centers,the spatial structure of the city is determined.This structure,in turn,influences commuting costs and agglomeration effects,leading to a redistribution of population and economic activities and,consequently,affecting residents’ welfare. The model reveals that increasing the number of sub-centers reduces commuting costs within the city.However,the dispersion of firms caused by a polycentric layout may weaken agglomeration economies.Urban planners must weigh these trade-offs and consider initial conditions such as natural geography and industrial bases to avoid coordination failures in sub-center development.Taking Chengdu as a case study,the paper calibrates the model and conducts counterfactual analyses to explore how different spatial planning strategies might reshape the distribution of population and economic activities,ultimately influencing residents’ welfare.
 The counterfactual analyses reveal that in monocentric cities,welfare levels are highly sensitive to changes in congestion factors,whereas polycentric cities are more resilient.Thus,under severe congestion,a polycentric layout offers significant potential to alleviate traffic-related challenges often faced by large cities.Moreover,Chengdu’s latest Land Spatial Master Plan (2020-2035),which integrates the existing western sub-center with the CBD area while prioritizing the development of eastern and southern sub-centers,appears well-positioned to enhance agglomeration effects,improve commuting efficiency,and elevate overall welfare.
 The findings provide valuable insights for urban policymakers in designing spatial plans.Furthermore,the proposed model and numerical simulation approach can be applied to other cities,offering a robust framework for evaluating and optimizing polycentric urban layouts—an increasingly critical policy instrument in the ongoing process of urbanization.

In the midst of digital revolution,it is both critical and urgent to promote the digital transformation of Chinese firms,as they face numerous obstacles in this process.These include,for example,a lack of clear strategic goals and practical pathways,a scarcity of digital talent,and insufficient financial resources.Emerging market governments,as a crucial force in market operations,play a significant role in coordinating and allocating market resources,while providing policy support and guidance.However,we know little regarding how local politician continuity,a systemic setting and political phenomenon in China,affects firm digital transformation.

Drawing on resource dependence theory,this study investigates the impact of local politician continuity on firm digital transformation.Politician continuity in a local area lowers uncertainty in public policymaking and resource allocation,thereby facilitating local firms’ digital transformation.Furthermore,firm specific characteristics and institutional factors determine the extent of firms’ resource dependence on the local government,thus moderating the aforementioned effect.Specifically,we focus on how the proportion of overseas revenue,research and development (R&D) investment,firm ownership,and local digital economy policies moderate the relationship between local politician continuity and firm digital transformation.

The empirical setting is Chinese listed firms from 2010 to 2022.Firm-level data came from the CSMAR database,Wind database,and China Research Data Service Platform (CNRDS).Information of local politicians were from the personal resumes of local officials published on websites,such as People’s Daily Online,Xinhua Net,Zecheng Net,and Baidu Encyclopedia,and are supplemented by news reports and government websites to ensure the comprehensiveness and authenticity of the data.The names,birth dates,genders,educational backgrounds,places of origin,time of joining the CPC,first working time,time of taking office,time of leaving office,reasons for leaving office,and other information are manually collected.In addition,other relevant data at the city level,such as the city’s gross domestic product and local digital economy policy orientation,was sourced from the China Statistical Yearbook and the China Research Data Service Platform.The final sample covers a total of 29967 observations for 4499 companies,involving a total of 249 prefecture-level cities.To validate the hypotheses,this paper constructs a fixed-effect Tobit model.The core explanatory variables and control variables were lagged by one year to promote the construction of causal relationships.

The study demonstrates that local politician continuity promotes digital transformation of firms within their jurisdictions.The proportion of overseas revenue and R&D intensity weaken the positive impact of local politician continuity while in state-owned firms,its impact gets more pronounced.Further,local digital economy policies weaken the effect of local politician continuity on firm digital transformation.

Additional analyses show that local politician continuity has a greater positive impact on firm digital transformation in central affiliated state-owned firms than local affiliated ones; and there is no difference between officials’ internal and external promotion.

Our study enriches the literature on politician continuity in two ways.①Based on resource dependence theory,we explicate how local politician continuity affects the digital transformation of firms within their jurisdictions.②We incorporate “central strategic objectives/local policy responses” into the theoretical framework of local politician continuity.We add to the emerging research on the antecedents of firm digitization transformation by providing a new perspective for understanding firms’ motivation for engaging in digital transformation.③By exploring the moderating effects of firm-level factors (i.e.,the proportion of overseas revenue and R&D investment) and institutional factors (i.e.,ownership and local digital economy policies),we demonstrate the importance of market strategies and institutional factors in managing non-market dependent relationships.

The findings of this study provide valuable management and policy insights.For managers,it is essential to actively manage the potential risks related to local politicians.In response to the negative impact of frequent local personnel changes on digital transformation,they can adopt market-oriented strategies such as increasing investment in overseas markets and R&D.For state-owned enterprises and firms located in areas with less digital economy policies,more attention should be paid to potential changes in local officials and sufficient preparations should be made to mitigate the negative impact on digital transformation.For governments:Firstly,it is necessary to enhance the continuity and stability of local policies during the transition period to provide a stable and predictable businessenvironment.Secondly,the study found that compared to state-owned enterprises,private enterprises still face significant disadvantages in accessing government resources.Although reducing dependence on the government mitigates the positive impact of local official continuity on their digital transformation,local governments should fully consider the actual needs of private enterprises,allocate resources reasonably,and improve resource utilization efficiency.Thirdly, it is crucial to strengthen policy guidance for local development of the digital economy.By enhancing policy supply in areas such as promoting industrial digitization,digital infrastructure construction,digital resource integration,digital talent cultivation,and network and information security system construction,the impact of local officials on firm digital transformation can be mitigated.

This paper examines the impact of China’s two-child policy on household risky asset allocation.Since the 1970s,China’s fertility rate has declined significantly,raising concerns about economic growth,such as an aging population and labor shortages.In response,the government relaxed birth restrictions,introducing the two-child policy to address these issues.Beyond influencing fertility intentions,the policy change may affect household financial decisions.Families anticipating higher child-rearing costs might seek to invest in riskier assets to improve returns,while others may reduce risky investments due to financial constraints.Understanding how fertility policy affects household investment choices is crucial for both policy design and the long-term sustainability of economic growth.Using the selective two-child policy in 2014 as a quasi-natural experiment,this study analyzes the China Household Finance Survey (CHFS) data to examine the causal relationship between the policy and household allocation of risky assets.

The findings indicate that the implementation of the two-child policy has led to a notable 7.3-percentage-point increase in the proportion of household risky assets,particularly in urban families.The study posits that families with stronger fertility preferences and higher income,who expect an increase in future child-rearing expenses,tend to invest in risky assets to increase their asset return rate.The research reveals that the increase in risky asset allocation is not due to new participation in risky asset investment,but rather an increase in risky asset allocation by households that already held risky assets and a decrease in the risk aversion of middle- and high-income households in urban areas.To test the robustness of the benchmark results,we used a series of robustness tests,including a placebo test,propensity score matching (PSM),exclusion of childless households,inclusion of rural households in the control group,addition of fixed effects,and control variables.All results are consistent with the baseline results.

In conclusion,this study finds that the implementation of the two-child policy has led to a significant increase in risky investment for eligible families.The rise in the risky investment rate is closely tied to family fertility preferences and income levels,establishing a logic chain of “relaxation of birth control-increase in fertility desire-increase in liquidity demand-increase in risky investment”.The research suggests that the increase in risky investment is due to residents’ anticipated increase in child-rearing expenses,leading to a preference for risky assets to increase asset returns.

The research contributes to the literature by linking fertility policy to household asset allocation and being the first to identify a causal effect of having an additional child on risky asset investment.It offers a new explanation for the low level of risky asset investment among Chinese households.The findings have important policy implications,highlighting the need for financial market reforms that take into account both the impact of population policies and the dynamic changes in household asset allocation.Policymakers should consider balancing the costs of child-rearing with opportunities for financial investment.Additionally,the government should strengthen financial market regulation to protect the rights of family investors,especially those with multiple children,and ensure greater fairness and transparency in the market.

The tourism industry has experienced sustained growth in recent years.However,the COVID-19 pandemic led to a sharp decline in global tourism in 2020.As the impact of the virus wanes and epidemic management becomes more standardized,the tourism sector is gradually rebounding.China,the world’s largest outbound tourism market,has significantly contributed to the global recovery of tourism through its policy approach to COVID-19 normalization.Forecasting tourist numbers enables more strategic allocation and adjustment of tourism resources,enhancing service quality.The pandemic has influenced tourism demand,making it essential to analyze these shifting patterns for the sustainable growth of the industry.

Research in tourism forecasting has yielded several insights:①Tourism demand forecasts benefit from incorporating relevant external factors,such as online search indices,to boost accuracy and interpretability.②Combining forecasting methods can enhance,or at least maintain,the accuracy of single forecasts. ③Unforeseen events like COVID-19 outbreaks can compromise the accuracy of traditional methods,necessitating specialized forecasting approaches during crises.However,existing methods often overlook the varying impact of exogenous factors on tourism demand over different periods,assuming a stable relationship between explanatory and target variables even in crisis conditions.In reality,the pandemic altered tourists’ risk perceptions,which,in turn,influenced their demand and consumption preferences,impacting their travel behavior.This indicates that an explanatory variable’s effect on tourism demand may differ across pre-pandemic,pandemic,and post-pandemic periods,meaning that variables from distinct periods may need to be treated as independent new variables.

This research primarily employed a Vector Autoregression model with exogenous variables (VARX),an adaptation of the Vector Autoregression (VAR) model that allows for the simultaneous analysis of endogenous and exogenous variables.The study began by gathering data on the number of Chinese outbound tourists,with a particular focus on trips to Japan,South Korea,and Singapore.Using web scraping techniques,55 Baidu search terms related to visa applications,trip planning,dining,accommodation,transportation,and shopping were collected; following correlation analysis,52 variables were retained.The classic factor model (FM) and dynamic factor model (DFM) were then used to combine the Baidu search indices into a composite indicator that retained dynamic relationships among the original multiple indicators.To account for the variable effects of exogenous factors over time,the Baidu search index was segmented into three phases—peak,trough,and recovery—based on tourists' search behaviors.Each segment served as an exogenous variable in the VARX model to forecast tourist numbers under different influences.Subsequently,a Stacking approach was applied in machine learning to combine various predictions,evaluated using RMSE,MAPE,and MASE.Finally,out-of-sample forecasts were produced to inform projections for Chinese outbound tourism.

The findings indicate that VARX models effectively predict Chinese outbound tourist numbers and that accounting for the time-specific effects of exogenous variables can enhance accuracy.Key insights include:①Aggregating new indicators from numerous web search indices is effective for high-dimensional time series forecasting,with the dynamic factor model proving superior in high-dimensional contexts,reducing prediction error and improving average accuracy by 12.25% over the classic factor model.Practically,managers can compile various search indices to create a comprehensive indicator that reflects market trends and use it to forecast tourism demand.② Segmenting variables based on significant time-based changes is practical,as the segmented combination model improves accuracy over traditional models and is valuable for tourism demand forecasting under crisis impacts.As tourism normalizes,people’s behavior and choices may carry over from previous patterns with some adjustments.③ The study not only evaluates the model’s validity but also projects an increase in Chinese outbound travel in 2024,expecting it to recover to at least 60% of pre-pandemic levels.

This study introduces a segmented combination forecasting approach for predicting Chinese outbound tourism.Utilizing a dynamic factor model to manage exogenous variables,it captures the original data’s multiple external factors while preserving their dynamic relationships.This method is not only relevant to tourism demand forecasting in special circumstances like pandemics but is also applicable in other contexts where data may be limited.

International organizations such as the G20 and OECD have been attempting to promote the adoption of the best corporate governance mechanisms across countries.As a result,many scholars have predicted that the corporate governance of companies in different countries would increasingly become similar.However,significant differences still exist in corporate governance among firms from different countries.Over the past three decades,scholars have attempted to examine the origins of these differences through large-sample cross-country comparative studies,which is defined as comparative corporate governance.However,the challenges of conducting solid comparative corporate governance research are substantial.Limited consensus exists over what factors best explain the diversity of corporate governance across countries.Moreover,few articles provide adequate attention to cross-country comparability testing,raising the concern that the reported findings may be spurious.In light of these opportunities and challenges,we systematically review comparative corporate governance studies published in top journals both domestically and internationally from 1990 to 2022.In this review,we not only draw on the available literature from international business (IB) and management but also borrow from comparative research in neighboring disciplines such as political science and sociology.The objectives of this review are three-fold:① take stock of the growing literature on comparative corporate governance by adapting Kohn’s typology of models of comparative research; ② provide a critical assessment of the literature and identify the gaps and problems in the extant studies; ③ set an agenda for future comparative corporate governance research.Drawing upon the cross-national comparative research framework proposed by sociologist Kohn,we categorize comparative corporate governance research into four domains:nation as the object of study,nation as the context of study,nation as the unit of analysis,and nation as part of a larger system.Firstly,13 studies hold a nation as the object of study.In this type of comparative corporate governance research,the authors’ primary interest lies in corporate governance in the particular countries studied.It is often descriptive,seeking to describe the similarities and/or differences in corporate governance in different nations.Secondly,18 studies have treated a nation as the context of the study.In such studies,the authors are primarily interested in examining whether and how national context is related to  corporate governance or  the relationship between corporate governance and its antecedents/consequences.Thirdly,research treating a nation as the unit of analysis has flourished in the comparative corporate governance field.In this type of research,the pivotal distinguishing national characteristics become variables in the analysis.Research that treats nations as units of analysis is typically labeled as “large-N comparative analysis”.Scholars pursuing this line of research are less interested in the unique context of the nations under study and more interested in the abstract relationships among quantifiable national characteristics.Finally,only four studies have treated nations as part of a larger international system.In these studies,scholars interpret a nation’s corporate governance as influenced by transnational systems or processes.

Based on our review of a sample of 115 papers,we identified significant  gaps and areas of concern that limit the impact and rigor of this line of research.Firstly,a deep understanding of the national context of corporate governance has been limited,which deserves further exploration.Secondly,scholars have also not paid enough attention to concept equivalence.Few comparative studies,particularly those treating a nation as the unit of analysis,have offered sufficient discussion of concept equivalence.Thirdly,the current literature has primarily focused on developed countries for comparative analysis,while relatively few studies have focused on emerging economies such as China.In addition,prior studies have adopted different methods and samples,making it difficult to determine whether the differences in corporate governance across countries are caused by variations in national institutions or by methods.Finally,although a number of scholars have effectively demonstrated key shortcomings in the law and finance approach,most scholars have predominantly relied on this approach to conduct comparative corporate governance research.

This paper has also proposed five avenues for future research based on the knowledge gaps identified in our critical assessment of the literature.① Scholars should conduct more systematic analyses of national context before translating “nations” into “variables.” Only with a deeper understanding of the national context can the country-level variables extracted from research become more explanatory.② Future research should take advantage of qualitative comparative analysis (QCA) and hierarchical linear modeling (HLM) in conducting comparative corporate governance research.③ Additional effort needs to be made to achieve comparability of concepts to enhance the reliability and validity of research.④ Future studies should present a justification for country selection.Scholars may only select countries to which they have access,which results in an over-representation of developed economies with better access to data on corporate governance.However,any similarities or differences revealed by a comparative corporate governance study may be no more than an artifact of the choice of countries.As a result,country selection must be theoretically justified.Given the significant differences in institutional environments and cultural contexts between emerging markets and developed countries,future research should incorporate emerging markets into the sample.⑤ Comparative corporate governance research can benefit from the  integration of theories from different disciplines.Therefore,future research should draw upon insights from other fields such as sociology and political science.

In recent years,China’s platform economy has developed rapidly,and its position and role in the overall economic and social development have become increasingly prominent.As a core element of the platform economy,the intrinsic relationship between Internet traffic and the platform economy needs to be examined.Internet Celebrity Economy/Influencer Economy refers to the phenomenon where creators/influencers on video-sharing and live-streaming platforms leverage their fame or talents to attract followers and then monetize the traffic through collaborations with businesses.The high loyalty,strong stickiness,and long retention time of the streamers’ fans significantly differentiate them from casual viewers.The direct competition among streamers underscores the importance of analyzing the oligopolistic nature of traffic entry points.

An interesting fact is that manufacturers of different categories often adopt different live-streaming e-commerce marketing models.Some product promotion links seem to be everywhere.Some merchants place advertisements on all platforms and multiple channels without distinguishing channel attributes.There even appears a contradictory situation where game zone anchors and knowledge zone anchors recommend the same product at the same time.Some merchants also place advertisements on multiple channels,but mainly in the form of pre-sales or flash sales,with a small amount of supply and no large-scale promotion.Other merchants focus on highlighting the “channel exclusive” feature in promotion,with limited release as the main selling point.

This paper argues that the differentiation of marketing models is related to product categories to a certain extent.Unlike registered users of traditional platforms,potential consumers in the field of live-streaming e-commerce often exist in the form of fans of anchors or brands.Fan attribute is the most distinct personal characteristic of the audience of live-streaming sales.The “die-hard” audience of anchors or brands stay in the live-streaming room frequently and for a long time,with high product exposure frequency,high trust in anchors or brands,and high possibility of accepting recommendations; The “passer-by” audience of anchors or brands do not have a strong awareness of the anchors or brands.They mostly enter the live-streaming entrance through the way of enterprise paid promotion of the live-streaming room,with a general stay time,and the possibility of accepting recommendations depends on the cost-effectiveness of the product; The “haters” of anchors or brands have a strong dislike for the anchors or brands,and it is almost impossible for them to become live-streaming audiences,let alone accept recommended marketing.The acquisition costs of these three types of users show significant horizontal differences.Hence,the recognition of commodity value by these three types of users,that is,the correlation between commodity preference and live-streaming audience,is the key to distinguish commodity categories.

Hence,this paper develops a duopoly model of platforms based on the Hotelling model,which allows firms to price traffic.It finds that when consumers’ preferences for goods are completely independent of their channel loyalty,firms always choose bilateral traffic diversion,and both partial and full market coverage are possible.When consumers’ preferences for goods are completely negatively correlated with their channel loyalty,the only possible scenario is bilateral traffic diversion by firms with partial market coverage.When consumers’ preferences for goods are completely positively correlated with their channel loyalty,two scenarios may occur:bilateral traffic diversion with full market coverage,or unilateral traffic diversion with partial market coverage.

Subsequently,this paper analyzes the impact of changes in platforms’ customer acquisition costs and market segmentation on social welfare.It provides targeted policy recommendations for various markets based on differences in consumer types:when consumers’ preferences for goods are completely independent of their channel loyalty,the degree of market coverage can be used as an intuitive indicator of welfare.Markets with higher coverage generally have better consumer welfare properties without losing too much total social surplus.When consumers’ preferences for goods are completely negatively correlated with their channel loyalty,central planners can restrict behaviors such as induced sharing and forwarding,and penalize false or exaggerated elements in promotional activities to reduce the willingness of undesired users to participate in marketing activities,thereby preventing firms from exiting the market.When consumers’ preferences for goods are completely positively correlated with their channel loyalty,central planners should control channels’ customer acquisition costs as much as possible to promote more transactions and should pay more attention to markets with lower segmentation,especially being vigilant against practices such as “big data price discrimination” that harm consumer rights.

This research not only has positive implications for guiding firms on how to choose appropriate traffic channel layouts but also fills a gap in the existing literature regarding the impact of the correlation between consumer preferences and traffic channel loyalty on market equilibrium.It provides a new perspective and explanatory framework for the theoretical study of platform economics.

Entrepreneurial activity across different cities in China exhibits significant regional disparities,with a distinct pattern of being “strong in the east and weak in the west.” Social organizations play a critical role in China’s socialist modernization efforts.As an important form of social organization,chambers of commerce serve as key bridges linking the government,enterprises,and markets.Chambers of commerce refer to legally registered social organizations composed of members engaged in similar economic activities,individuals,or economic entities within the same region,operating under principles of industry service and self-regulation.

In recent years,regional chambers of commerce in China have experienced rapid growth,playing an increasingly prominent role in supporting high-quality development.According to data from the China Social Organization Government Service Platform,as of December 2023,there were 24,970 registered regional chambers of commerce below the provincial level (excluding provincial-level chambers of commerce).However,the regional pattern of “strong in the east and weak in the west” remains prominent.This raises several critical questions:Can regional chambers of commerce enhance urban entrepreneurial activity? If a positive effect exists,what mechanisms drive it? What factors may strengthen or constrain the entrepreneurial impact of these chambers of commerce? Analyzing these questions not only contributes to understanding the theoretical and practical significance of regional chambers of commerce but also provides policy insights for optimizing the entrepreneurial environment and promoting employment.

Based on data manually compiled from the China Social Organization Government Service Platform on regional chambers of commerce below the provincial level,matched with the 2003-2021 China Industrial and Commercial Registration Database,this study empirically examines the impact of regional chambers of commerce on urban entrepreneurial activity.The findings reveal that regional chambers of commerce significantly enhance entrepreneurial activity in cities.Specifically,an increase of one standard deviation in the cumulative number of regional chambers of commerce per 100,000 people results in an increase of 0.419 newly registered enterprises per 100 people in a city.

The study conducted a series of robustness checks,including differentiating among types of chambers of commerce,replacing measures of urban entrepreneurial activity,modifying indicators of regional chambers of commerce development,and adjusting the research sample scope.All results consistently support the robustness of the main conclusions.To address potential endogeneity concerns,the study employs the number of chambers of commerce and river density in each prefecture-level city as of 1912 as instrumental variables for regional chambers of commerce.

Mechanism analysis indicates that regional chambers of commerce enhance urban entrepreneurial activity by improving social trust and optimizing the business environment.Further,the study examines the heterogeneous impacts of regional chambers of commerce from multiple dimensions,including ownership types,industry classifications,and city types.The results demonstrate that regional chambers of commerce primarily promote the entry of individual industrial and commercial households and private enterprises,have the most significant impact on labor-intensive industries,and exhibit stronger entrepreneurial effects in third-tier and smaller cities,non-urban agglomeration cities,and small to medium-sized cities.Additionally,the study finds that regional chambers of commerce generate synergistic effects with entrepreneurship-related policies,thereby boosting urban entrepreneurial activity and enhancing urban innovation levels.

Compared to existing research,this study offers three main contributions:

(1) Existing empirical studies on chambers of commerce primarily focus on provincial-level nonlocal chambers of commerce in China,with limited discussion on local chambers of commerce and inadequate systematic analysis of regional chambers of commerce below the provincial level due to data constraints.This study is the first to extend the research perspective to regional chambers of commerce below the provincial level nationwide,encompassing both local and nonlocal chambers of commerce.It refines the spatial scale to include municipal-level,county-level,township-level,and neighborhood associations.

(2) Previous literature has explored the impact of provincial-level nonlocal chambers of commerce on interregional enterprise development,cross-regional trade,and interprovincial labor mobility.This study enriches the empirical literature on the economic effects of chambers of commerce by demonstrating that regional chambers of commerce significantly enhance urban entrepreneurial activity.

(3) The study investigates the specific mechanisms through which regional chambers of commerce influence urban entrepreneurial activity,showing that these chambers of commerce enhance social trust and optimize the business environment.These findings deepen the understanding of regional chambers of commerce as a vital form of social organization and enrich research on factors influencing urban entrepreneurial activity.The findings of this study provide valuable insights for fully leveraging the positive role of regional chambers of commerce,optimizing urban entrepreneurial environments,and promoting high-quality economic development.

The issuance of the CSI A500 ETF has shifted market attention towards passive investment strategies.ETFs,as a well-established passive investment tool,have garnered increasing attention due to their unique and innovative trading attributes.This paper investigates whether passive index investment tools enhance market monitoring functions from the perspective of ETF ownership.The findings reveal that a higher proportion of ETF ownership in individual stocks significantly reduces the prevalence of shareholder equity pledging.An exploration of the underlying mechanisms suggests that higher ETF ownership mitigates information delay and offsets existing noise trading,thereby improving stock price information efficiency.This enhanced efficiency generates a monitoring effect,ultimately reducing equity pledging by shareholders.In terms of ownership characteristics,the monitoring effect is more significant in firms where the largest shareholder holds a higher ownership stake or in non-SOE enterprises.Further,the improvement effects brought about by ETFs are more pronounced in firms with lower levels of information disclosure quality.The results of the study remain robust after undergoing various tests,including instrumental variable analysis.The conclusions of this study provide important insights for improving market efficiency,strengthening market monitoring functions,and fostering a virtuous governance cycle.

We primarily examine the enhancement of market monitoring functions from the perspective of ETF ownership,using equity pledge as a measure of the effectiveness of market supervision.The interaction between institutional investors and corporate governance generally operates through three mechanisms:active governance,“voting with their feet”,and market-based information mechanisms for indirect supervision.Given the diverse investor base and dispersed composition of ETFs,we argue that the third mechanism predominantly underpins their market monitoring role.We investigate whether an increase in the proportion of ETF holdings can reduce equity pledging,thereby curbing corporate tunneling behaviors and enhancing effective monitoring.We identify improvements in stock price information efficiency as the primary channel through which ETFs exert their supervisory function.Key factors influencing stock price information efficiency include the speed of information transmission,liquidity depth,and the proportion of noise trading.Our empirical analysis demonstrates that the introduction of ETFs accelerates information transmission,offsets existing noise trading,and enhances the efficiency of stock price information.This,in turn,creates capital market pressure that imposes supervisory constraints on equity pledging by shareholders,although we find that the liquidity channel appears less significant.Further analysis reveals that the supervisory effect of increased ETF ownership is more evident in firms where the largest shareholder holds a higher proportion of shares or in non-state-owned enterprises,as these firms often exhibit stronger incentives for tunneling.We also find that the effects of ETFs are more pronounced and significant in firms with weaker information disclosure practices.To address potential endogeneity concerns,we leverage the exogenous increase in passive ownership resulting from index reconstitutions,referring to changes in the bottom constituents of the Russell 1000 Index and the top constituents of the Russell 2000 Index.Similarly,in the Chinese market,we use changes in the CSI 300 and CSI 500 indices,and construct an instrumental variable based on newly added top-tier constituents in the CSI 500 Index.Our results remain robust,thereby ensuring the reliability of our findings.

The primary contributions of this paper are as follows:1)Expanding the understanding of the impact of passive index investment tools from the perspective of ETFs:While existing literature on ETFs largely focuses on their influence on the characteristics of capital markets,there is a lack of in-depth exploration of their effects on corporate governance.Our findings provide a robust interpretation of the role ETFs play in corporate governance,demonstrating how ETF ownership influences equity pledging through its effects on the informational environment.This contribution offers new insights and evidence regarding how financial instruments can address corporate governance issues via information mechanisms.2)Augmenting research on the factors influencing equity pledging:Prior studies on equity pledging have predominantly focused on its consequences or subsequent abnormal behaviors,leaving the determinants of equity pledging relatively underexplored.By adopting the unique perspective of ETFs,our study identifies how different informational environments affect equity pledging decisions,filling an important gap in the literature.3)Exploring the channels through which ETFs exert influence:We show that ETFs play a role in corporate governance by reducing information transmission lags and mitigating noise trading.This enriches the understanding of the mechanisms through which passive index investment tools operate.Given the growing scale of ETF investments,our findings highlight the practical importance of examining how ETFs shape the ecological dynamics of capital markets.Overall,the results of this study have significant implications for future research on passive index investment tools.They contribute to a deeper understanding of how such tools enhance market efficiency and foster greater market depth,offering valuable guidance for both academic inquiry and practical application.