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Sponsor: Shaanxi Provincial Department of Education
Sponsored by：Xi'an University of Finance and Economics
Higher Education Branch of China Statistical Education Society
Director: ZHAO MINJUAN
Vice Director: LI JIAORUI ZHAO YANYUN
Publisher: Editorial Department of Journal of Statistics and Information
Address:No. 64, XiaozhaiEast Road, Yanta District, Xi’an, China
Post Code：710061
E-mail: tjyxxlt@126.com
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Theoretical Foundations and Progress Assessment of the Evaluation Index System for Chinese Modernization
FANG Kuangnan;WANG Xiaoying;WANG Lu;CHEN Ying;Chinese modernization emphasizes both the critical assimilation of common features observed in global modernization processes and the implementation of adaptive adjustments aligned with national conditions.It calls for the construction of a scientific,dynamic,and open evaluation framework with distinctive Chinese characteristics,capable of objectively reflecting the historical stage and developmental trajectory of modernization in China.For a considerable period,discussions on Chinese modernization have largely remained at the level of theoretical interpretation,with insufficient empirical and quantitative evidence to support precise descriptions of real-world progress.Grounded in the connotations and objectives of socialist modernization with Chinese characteristics,the conceptual pathway for building an evaluation index system of Chinese modernization is systematically examined.The framework adopts the “five defining features” of Chinese modernization as primary indicators,further developing a structured system comprising 15 secondary indicators and 59 tertiary indicators.This multi-layered index system provides a comprehensive and measurable approach to capturing the complexity and multidimensionality of modernization in the Chinese context.Based on this evaluation framework,the annual evolution trajectory of the Chinese modernization index from 2012 to 2023 at the national level is calculated and analyzed.The results reveal both the underlying mechanisms of change and the broader developmental trends embedded within the modernization process.Findings indicate that the historical progression of Chinese modernization demonstrates an irreversible and forward-moving trend.However,this trajectory is shaped by multiple constraints,including the availability of development resources,external geopolitical influences,and the necessity of internal structural adjustments.As a result,the overall pattern is characterized by steady advancement accompanied by occasional fluctuations.In addition,unexpected public events,exemplified by the COVID-19 pandemic,exert notable shock effects on the modernization process.Such disruptions temporarily alter developmental dynamics and introduce uncertainties into the trajectory of progress.Nevertheless,the inherent institutional resilience embedded within China's governance system plays a significant buffering role,mitigating adverse impacts and facilitating recovery.This resilience not only stabilizes short-term fluctuations but also reinforces long-term continuity in modernization efforts.The analysis highlights that the sustainability and stability of Chinese modernization are closely linked to its institutional foundations,which provide both adaptability and robustness in the face of internal and external challenges.The proposed evaluation index system contributes to bridging the gap between theoretical discourse and empirical assessment,offering apractical tool for monitoring,analyzing,and guiding modernization.By enabling a more precise and data-driven understanding of developmental stages and progress,it supports informed decision-making and enhances the capacity to respond effectively to evolving conditions.
Research on the Impact of New-type Infrastructure on Industrial Structure Upgrading
MA Wei;HUI Ning;China's economy has entered a stage of high-quality development.The continuous optimization of the economic structure is a prerequisite for steadily improving the quality and efficiency of economic growth.Industrial structure upgrading under high-quality development requires new drivers of growth.New-type infrastructure serves as a pivotal force in the digital economy era,comprehensively reshaping production relations and unleashing digital productivity.It can inject powerful momentum into China's efforts to accelerate the formation of new quality productive forces and achieve industrial structure upgrading.Therefore,this paper starts from labor transfer channels and uses a three-dimensional analysis framework to explain the theoretical mechanism by which new-type infrastructure affects industrial structure upgrading,and proposes corresponding research hypotheses accordingly.Using panel data from284 prefecture-level and above cities in China from 2003 to 2023,this paper empirically studies the impact of new-type infrastructure on industrial structure upgrading,as well as the underlying transmission mechanisms,based on measuring the level of new-type infrastructure construction at the city level.The results show that new-type infrastructure has significantly promoted the upgrading of industrial structure.Specifically,the three categories of new-type infrastructure—information infrastructure,innovation infrastructure,and integrated infrastructure—each exhibit a clear enabling effect.This conclusion remains robust after taking into account the interference of endogeneity and undergoing a series of robustness tests.Mechanism analysis shows that new-type infrastructure enables China's industrial structure upgrading through labor transfer channels,including deepened labor division,optimized labor supply,and improved labor welfare.Heterogeneity analysis finds that the positive impact of new-type infrastructure on industrial structure upgrading is more pronounced in cities with stronger digital governance effectiveness and those at the two extremes of the industrial structure upgrading level.The findings not only provide empirical evidence for understanding the institutional environmental conditions under which new-type infrastructure plays a role,but also capture the structural characteristics of new-type infrastructure at different levels of industrial structure upgrading,which include “providing timely help”,“marginal optimization”,and “adding icing on the cake”.This breaks the traditional perception of “inclusive” impact and provides a key basis for formulating differentiated and precise policies.The results of the spatial effect test show that endogenous spatial interaction effects in industrial structure upgrading objectively exist,and the positive spatial spillover effects of new-type infrastructure in the process of industrial structure upgrading have also been confirmed.Accordingly,it is proposed that while increasing investment in various types of new-type infrastructure,exploring differentiated development strategies for new-type infrastructure,unblocking channels for labor transfer,and maximizing the spatial positive externalities of new-type infrastructure are the key to driving industrial structure upgrading.
Value Chain Agglomeration in the Digital Industry: Evolutionary Patterns and Shifting Drivers
ZHENG Linchang;XUE Binghua;CHEN Ge;Currently,enterprises in certain emerging industries collaborate along the value chain,creating localized “value concentration”.This phenomenon urgently requires a theoretical explanation regarding its underlying drivers and determinants.Drawing on 2015-2023 firm-level data from Shanghai and Shenzhen A-share listed companies and the New Third Board,this study calculates a city-level value chain agglomeration index for the digital industry and employs a two-way fixed-effects model to examine how emerging determinants and general factors impact the agglomeration of value chain segments in the digital industry,alongside their heterogeneous effects across cities.The results indicate that:(1)China's digital industry exhibits a prominent division of labor along the value chain and distinct characteristics of the agglomeration of value chain segments.(2)The industry demonstrates a transitional trend from the“geographic clustering of industrial activities” to the “agglomeration of value chain segments”.(3)The value economy and virtual economy have become core drivers of this agglomeration.Emerging determinants,particularly technological innovation,have replaced general factors as the key variables driving the agglomeration of value chain segments in the digital industry.(4)The promoting effect of emerging determinants on this agglomeration is more pronounced in urban agglomerations,large cities,and cities with superior digital environments.As the forms,drivers,and determinants of China's digital industry agglomeration have undergone transformation,localities must innovate agglomeration management models,tailor digital development to local conditions,and enhance digital infrastructure and technological innovation capabilities.
Research on Dynamic Economic Shock Effects of Industry Tail Risk Transmission:From a Production Network Perspective
REN Junfan;CUI Yanxin;CHEN Taofeng;XU Xiangyun;Methodologically,this investigation develops an innovative framework by incorporating an industrial tail risk factor into a comprehensive production network model.This sophisticated modeling strategy successfully identifies and distinguishes between two fundamental transmission mechanisms:the technical distortion network effect,which manifests through the propagation of productivity inefficiencies across interconnected industries,and the allocation distortion network effect,which operates through the cascading consequences of resource misallocation throughout the production ecosystem.The empirical analysis utilizes extensive datasets covering 44 distinct Chinese industries over the 1998-2020 period,meticulously compiled from the authoritative Inter-Country Input-Output(ICIO)database.To ensure robust examination of dynamic relationships,the research implements advanced econometric methodologies including Panel Vector Autoregression(PVAR)models and Time-Varying Parameter Stochastic Volatility Vector Autoregression(TVP-SV-VAR)techniques,enabling precise quantification of the differential temporal impacts exerted by these dual distortion effects on industrial value-added fluctuations.The empirical findings reveal several pivotal conclusions.First,both technical and allocation distortion network effects generate substantial short-term adverse consequences for China's economic performance,with the allocation distortion effect demonstrating particularly severe and immediate contractionary impacts due to its fundamental disruption of resource allocation efficiency.Second,the analysis identifies intermediate input substitutability as a crucial mitigating factor,suggesting that enhancing flexibility in production input utilization can effectively buffer against the negative value-added consequences of network-transmitted distortions.Third,and most significantly,extreme tail events function as powerful amplifiers of these transmission mechanisms,where even isolated technical deficiencies or resource misallocations within single industries can trigger extensive multiplier effects through input-output connections,ultimately generating persistent value-added deterioration across multiple industrial chain echelons.These insights fundamentally underscore the critical importance of recognizing production networks as potent conduits for risk amplification within modern economic systems.Consequently,the study proposes a comprehensive policy framework emphasizing the establishment of sophisticated risk monitoring and early-warning systems,strategic enhancement of supply chain resilience and adaptive capacity,systematic optimization of resource allocation mechanisms,and accelerated technological innovation and adoption.The effective implementation of these coordinated policy measures represents an essential prerequisite for constructing a secure,resilient,and self-reliant economic development paradigm capable of navigating the complex challenges characterizing the contemporary global economic landscape.
Can Tax Incentives Promote Firms' Artificial Intelligence Technological Innovation?
LYU Guangming;XIAO Shuangshuang;As a strategic technology leading and driving future development,artificial intelligence(AI)innovation has become a key policy focus for fostering new quality productive forces at the firm level.Using a sample of Chinese A-share listed companies in the Shanghai and Shenzhen stock markets from 2007 to2022,this study systematically examines the impact of the accelerated depreciation policy for fixed assets,as a tax incentive,on corporate AI innovation by employing a staggered difference-in-differences model.The results show that the accelerated depreciation policy for fixed assets significantly promotes corporate AI innovation.Heterogeneity analysis further indicates that the policy effect is more pronounced among state-owned enterprises,firms located in regions with a higher degree of marketization,and firms with higher levels of human capital.Mechanism tests reveal that the policy promotes corporate AI innovation through three channels,namely improving the degree of data asset capitalization,increasing AI investment,and enhancing technological breakthroughs,while corporate operating efficiency plays a positive moderating role in this process.These findings provide important policy implications for how governments can leverage tax instruments in the digital and intelligent era to facilitate the accelerated development of new quality productive forces.
Policy Mixes for Artifical Intelligence Innovation Performance
YAN Xiaotong;TANG Xiaobin;LIU Siming;Enhancing AI innovation performance through a new system for mobilizing resources nationwide,which emphasizes innovations in organizational structures and operational logic rather than administrative commands,has become a critical issue.From a policy mix perspective,with a focus on 19 cities designated as national AI pilot zones, fuzzy-set qualitative comparative analysis(fsQCA) is integrated with Bayesian network modeling.This integration identifies configurational paths and probabilistic causal mechanisms linking supply-side,demand-side,and environmental policy instruments to AI innovation performance.Results indicate:First,no single policy instrument acts as a necessary condition for achieving high innovation performance.This finding highlights the limitations of isolated policy interventions and underscores the critical necessity of exploring multiple, synergistic policy mix models.Success depends not on individual tools but on their holistic alignment.Second,the study identifies three distinct configurations leading to high AI innovation performance:the "Coordinated Leadership " type,emphasizing factor coordination and resource integration;the "Institutional Empowerment " type,focusing on rule-based guidance and ecosystem synergy;and the "Foundation-Driven" type,prioritizing solidifying foundational capabilities and fostering market-driven dynamism.These patterns illustrate the principle of equifinality,demonstrating that pilot cities can achieve high performance through diverse pathways tailored to their specific contexts.Conversely, the identification of three configurations associated with non-high innovation performance underscores the importance of synergistic policy combinations, highlighting the necessity of coordinated use of diverse policy instruments.Third, the research elucidates that government and market forces do not operate in isolation.Instead,they co-evolve through direct and indirect synergistic mechanisms to support AI innovation.The government provides strategic direction,public goods,and risk mitigation,while the market drives efficiency,commercialization,and dynamic resource allocation.The findings not only verify the impact of multiple policy mixes on AI innovation performance, but also offer theoretical and practical insights into effectively leveraging the new system for mobilizing resources nationwide to orchestrate government roles and invigorate market forces for AI development.
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ESG Performance and Corporate Export Behavior
WANG Jiaqi;LANG Lihua;CHU Tingting;As a crucial criterion for measuring sustainable development capabilities,ESG has a profound impact on the export behavior of enterprises.Based on the panel data of Chinese listed companies from 2009 to 2023,the impact of ESG performance on corporate export behavior and its underlying mechanisms is empirically examined from two dimensions:export propensity and export scale.The research finds that good ESG performance can promote enterprises to choose to export and expand their export scale.After robustness tests such as replacing core explanatory variables,changing model estimation,removing special samples,and excluding policy interference,endogeneity treatments such as instrumental variable estimation,Heckman two-stage regression,and PSM-DID tests,the conclusion remains valid.Further,ESG performance exerts a direct positive impact on the export behavior of enterprises from the environmental,social,and governance dimensions,with the environmental dimension having the most prominent effect.Mechanism analysis indicates that ESG performance can drive the export behavior of enterprises through the mechanism channels of enhancing innovation capabilities and optimizing supply chain structures.Heterogeneity analysis reveals that the export behavior promotion effect of ESG performance is more significant in state-owned enterprises,technology-intensive enterprises,and enterprises in regions with high marketization.Additionally,internal digital transformation and external media attention can enhance the positive effect of ESG performance on export behavior;ESG performance also exhibits peer effects at the industry and provincial levels,further promoting the export behavior of enterprises.Therefore,this study suggests that efforts should be made to actively build ESG advantages,enhance the ESG awareness and capabilities of various types of enterprises,and strengthen the export promotion role of ESG through the dual engines of internal digital transformation and external media attention.The research not only expands the research framework of ESG in the trade field but also provides important policy implications for enterprises to achieve sustainable exports in a complex international environment.
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Real-Time Forecasting and Early Warning Information Identification of Economic Growth Risks in China
WANG Lijun;LIU Han;Accurately forecasting economic risks and identifying early warning signals are crucial for short-term policy regulation as well as long-term high-quality development.This study develops a machine learning framework for risk forecasting and early warning signal identification, integrating multidistribution, high-dimensional,which enables real-time forecasting of economic growth risks and screening of early warning information for China.The empirical results show that,first, the proposed MF-QRLSTM model based on the Sinh-Arcsinh distribution and its combination with the skew-t distribution significantly improves the real-time prediction accuracy of economic growth risks compared to traditional GaR methods.This improvement is achieved through integration of high-dimensional mixed-frequency data and precise characterization of nonlinear dynamic relationships among variables.Second,the marginal predictive contribution of financial conditions to economic growth risks exhibits clear state dependence:their contribution is relatively weak during periods of economic stability,but provides significantly more incremental predictive information during periods of economic shocks.Third,real economic development,domestic and international financial conditions,and macroeconomic expectations are key factors influencing the prediction of economic growth risks and can provide important early warning signals.The findings of this study offer valuable insights for building a risk monitoring and early warning system for economic growth under the new circumstances.
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Statistical Inference Research on the Low-carbon Transformation of Energy Enterprises Driven by Green Financial
LIU Meng;GUO Linxin;CHEN Tingqiang;The green and low-carbon development is central to China ' s ecological civilization modernization.The driving mechanisms and pathways of green finance for energy enterprises exhibit significant heterogeneity.This study employs natural language text recognition technology and constructs an index for low-carbon transformation of energy enterprises and an index for urban green finance development using the entropy-weighted method.Based on sample data from 158 listed companies in China's energy sector from 2012 to 2023,this study empirically examines the driving mechanisms of green finance on the low-carbon transformation of energy enterprises.The findings indicate that green finance serves as a crucial driver for promoting green transformation in energy enterprises,primarily by fostering technological innovation and alleviating financing constraints;Additionally,environmental regulations can synergistically enhance the green transformation of energy enterprises alongside green finance,while also playing a significant moderating role in shaping the transformation pathways.Additionally,green finance demonstrates notable heterogeneity in promoting low-carbon transformation across time,regions,industries,and concentration levels,with the most pronounced effects observed during 2012-2015,in central and western regions,in traditional energy sectors,and among enterprises with low industry concentration.These results highlight the importance of establishing specialized financial instruments and environmental regulation policies for energy enterprises,strengthening government-enterprise collaboration,and accelerating the standardization of green finance and environmental regulations.Such efforts are essential to promote low-carbon transformation and advance the modernization of the energy sector.
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Research on Interval Time Series Forecasting for Addressing Information Redundancy and Pseudo-interval Issues
WANG Piao;TAO Zhifu;LIU Jinpei;Compared with traditional point data,interval data can depict the dynamic change process of data more completely,and has important application value in fields like economic forecasting and environmental monitoring.However,existing interval data forecasting methods generally have two core problems:first, they fail to fully utilize the inherent similarity of upper and lower bound information of intervals, leading to information redundancy;second,forecasting results are prone to "pseudo-intervals"(with reversed upper and lower bounds), which seriously impair forecasting effectiveness.Based on this,a new interval time series forecasting method is proposed integrating PC A dimensionality reduction and reconstruction.In the research process, first, PC A is used to convert interval data into a one-dimensional time series, which retains over 95 % of key information of the original data and effectively solves the information redundancy problem.Second, under the assumption that the original data distribution remains stable,common forecasting methods are adopted to predict the dimensionally reduced series,and PC A inverse transformation is applied to realize interval reconstruction,which fundamentally avoids pseudointerval generation.Finally,for multivariate scenarios,it completes modeling through one-time information integration,significantly reducing error accumulation caused by repeated data use.Meanwhile,Pseudointerval Rate(PIR),Prediction Interval Coverage Probability(PICP) and Mean Normalized Prediction Interval Width(MNPIW) are introduced to construct a multi-dimensional evaluation system for comprehensively measuring forecasting performance.Empirical results and analysis show that while solving information redundancy and pseudo-interval problems,it significantly improves the forecasting accuracy of interval time series,provides a new idea with both theoretical innovation and practical feasibility for interval time series forecasting research,and offers more reliable technical support for decision-making based on interval data in related fields.
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A Review of Technologies on Random Forests
FANG Kuang-nana,b,WU Jian-bina,ZHU Jian-pinga,b,SHIA Bang-changa,b(a.Department of Statistics,School of Economics;b.Data Mining Center,Xiamen University,Xiamen 361005,China)Random Forests is a statistical learning theory,using bootsrap re-sampling method form sample sets,and then combining the tree predictors by majority voting so that each tree is grown using a new bootstrap training set.It is widely applied in medicine,bioinformatics,economics and other fields,because of its high prediction accuracy,good tolerance of noisy data,and the law of large numbers they do not overfit.In this paper we first introduce the concept of random forest and the latest research,then provide some important aspects of applications in economics,and a summary is given in the final section.
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Analysis on the Reliability and Validity of Questionnaire
ZENG Wu-yi~1,HUANG Bing-yi~2(1.School of Economics,Xiamen University,Xiamen 361005,Fujian;2.School of Management,Xiamen University,Xiamen 361005,Fujian)Study on the reliability and validity of the questionnaire has always been neglected in many(questionnaire) surveys in China.This paper mainly investigates the reliability and validity of a questionnaire and their evaluating methods.It also simply introduces how to use SPSS software to analyze the reliability and(validity) of a questionnaire.
ESG Performance,Institutional Investor Preference and Firm Value of Listed Companies
BAI Xiong;ZHU Yi-fan;HAN Jin-mian;To explore whether the ESG practices of listed companies can create value for the company and whether institutional investors in the capital market have ESG investment preferences will help companies recognize, participate in and practice the concept of ESG sustainable development.Based on the data of 3 400 A-share listed companies in Shanghai and Shenzhen Stock Exchange from 2013 to 2020,the shareholding ratio of institutional investors is introduced to explore the mechanism of ESG performance affecting corporate value and analyze whether institutional investors have ESG investment preference on this basis.The results are as follows:(1) ESG has the function of value creation.Good ESG performance of listed companies can significantly enhance their corporate value.(2) Attracting institutional investors to increase their shares is one of the ways for listed companies to enhance corporate value through ESG practice, and the proportion of institutional investors plays a partial intermediary role in the process of ESG influencing corporate value.(3) Institutional investors have a preference for ESG investment, and to a certain extent, they can tolerate low short-term operating performance of listed companies with good ESG performance All the above conclusions are robust.In the extended study, it is found that there is no heterogeneity in the value creation function of ESG between state-owned and non-state-owned listed companies.The preference of institutional investors ESG has heterogeneity in property rights and industry.Institutional investors prefer the listed companies with good performance of ESG in the secondary and tertiary industries and non-state-owned enterprises.Based on the research conclusions, suggestions are puts forward to accelerating the top-level design of ESG information disclosure and regulatory standards, encouraging companies to strengthen information disclosure, and cultivating medium and long-term institutional investors, which will help build and improve China's ESG development ecosystem and promote high-quality development.
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A Summary of Machine Learning and Related Algorithms
CHEN Kai1,ZHU Yu1,2(1.School of Statistics,Renmin University of China,Beijing 100872,China;2.Xi'an University of Finance & Economic,Xi'an 710061,China)Since the computer was invented,people have been wanted to know that whether it can learn.Machine learning is essentially a multidisciplinary field. It absorbed some results of artificial intelligence,probability and statistics,computational complexity theory,control theory,information theory,philosophy,physiology,neurobiological.This paper mainly based on statistical learning wanted to give a brief review and presentation to the perspective of machine learning and the development of related algorithms.
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Measurement of China's Provincial Digital Economy and Its Spatial Correlation
JIN Can-yang;XU Ai-ting;QIU Ke-yang;Based on the input-output perspective of economic systems, the index measurement system of digital economy development level is constructed from five dimensions: digital infrastructure, digital innovation, digital governance, digital industrialization and industrial digitization by combining the fuzzy set idea, and the weights are determined and compiled with the help of the vertical and horizontal pull-off method for China's provincial digital economy development index from 2012 to 2019.Based on this, the modified gravitation model is used to measure the spatial correlation intensity of the provincial digital economy development level, and the social network analysis is used to reveal the overall shape, internal structure, and evolutionary trend of the digital economy correlation network.The results are shown as follows.(1) The overall development of the digital economy across the country is on the rise, but the “Matthew effect” and “digital divide” are obvious, with the level of digital economy development decreasing from the eastern coast to the western inland.(2) The initial formation of a network of digital economy linkages, the agglomeration and spillover effects in various regions have gradually increased, and the mobility of digital resource elements in the province has been greatly enhanced.(3) Guangdong, Jiangsu, Beijing and other eastern provinces, as structural hole occupiers, have information and resource control advantages in the development of the digital economy, and Henan, Shaanxi and Sichuan, which have faster rate of effective scale and limit system enhancement, are seen as potential occupiers of structural holes.(4) The development of the digital economy is characterized by a clear aggregation of small groups, with four cohesive subgroups formed at the provincial level, and the linkage within the subgroups is significantly stronger than the external influence.(5) Due to geographical location, climatic conditions and other factors, there is less communication among members within the Northwest subgroup, and the density within its subgroup is lower than that of the whole network, and its internal digital economy tie needs to be further strengthened.The research findings have important implications for promoting the construction of a new pattern of digital economy development in China.
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A Review of Technologies on Random Forests
FANG Kuang-nana,b,WU Jian-bina,ZHU Jian-pinga,b,SHIA Bang-changa,b(a.Department of Statistics,School of Economics;b.Data Mining Center,Xiamen University,Xiamen 361005,China)Random Forests is a statistical learning theory,using bootsrap re-sampling method form sample sets,and then combining the tree predictors by majority voting so that each tree is grown using a new bootstrap training set.It is widely applied in medicine,bioinformatics,economics and other fields,because of its high prediction accuracy,good tolerance of noisy data,and the law of large numbers they do not overfit.In this paper we first introduce the concept of random forest and the latest research,then provide some important aspects of applications in economics,and a summary is given in the final section.
[Downloads: 48,490 ] [Citations: 2,563 ] [Reads: 116 ] HTML PDF Cite this article
A Summary of Machine Learning and Related Algorithms
CHEN Kai1,ZHU Yu1,2(1.School of Statistics,Renmin University of China,Beijing 100872,China;2.Xi'an University of Finance & Economic,Xi'an 710061,China)Since the computer was invented,people have been wanted to know that whether it can learn.Machine learning is essentially a multidisciplinary field. It absorbed some results of artificial intelligence,probability and statistics,computational complexity theory,control theory,information theory,philosophy,physiology,neurobiological.This paper mainly based on statistical learning wanted to give a brief review and presentation to the perspective of machine learning and the development of related algorithms.
[Downloads: 28,808 ] [Citations: 681 ] [Reads: 138 ] HTML PDF Cite this article
Analysis on the Reliability and Validity of Questionnaire
ZENG Wu-yi~1,HUANG Bing-yi~2(1.School of Economics,Xiamen University,Xiamen 361005,Fujian;2.School of Management,Xiamen University,Xiamen 361005,Fujian)Study on the reliability and validity of the questionnaire has always been neglected in many(questionnaire) surveys in China.This paper mainly investigates the reliability and validity of a questionnaire and their evaluating methods.It also simply introduces how to use SPSS software to analyze the reliability and(validity) of a questionnaire.
ESG Performance,Institutional Investor Preference and Firm Value of Listed Companies
BAI Xiong;ZHU Yi-fan;HAN Jin-mian;To explore whether the ESG practices of listed companies can create value for the company and whether institutional investors in the capital market have ESG investment preferences will help companies recognize, participate in and practice the concept of ESG sustainable development.Based on the data of 3 400 A-share listed companies in Shanghai and Shenzhen Stock Exchange from 2013 to 2020,the shareholding ratio of institutional investors is introduced to explore the mechanism of ESG performance affecting corporate value and analyze whether institutional investors have ESG investment preference on this basis.The results are as follows:(1) ESG has the function of value creation.Good ESG performance of listed companies can significantly enhance their corporate value.(2) Attracting institutional investors to increase their shares is one of the ways for listed companies to enhance corporate value through ESG practice, and the proportion of institutional investors plays a partial intermediary role in the process of ESG influencing corporate value.(3) Institutional investors have a preference for ESG investment, and to a certain extent, they can tolerate low short-term operating performance of listed companies with good ESG performance All the above conclusions are robust.In the extended study, it is found that there is no heterogeneity in the value creation function of ESG between state-owned and non-state-owned listed companies.The preference of institutional investors ESG has heterogeneity in property rights and industry.Institutional investors prefer the listed companies with good performance of ESG in the secondary and tertiary industries and non-state-owned enterprises.Based on the research conclusions, suggestions are puts forward to accelerating the top-level design of ESG information disclosure and regulatory standards, encouraging companies to strengthen information disclosure, and cultivating medium and long-term institutional investors, which will help build and improve China's ESG development ecosystem and promote high-quality development.
[Downloads: 23,880 ] [Citations: 879 ] [Reads: 94 ] HTML PDF Cite this article
Research on the Impact of R&D Investment and Government Subsidy on Enterprise Innovation Performance
WANG Xi;ZHANG Qiang;HOU Jia-xiao;China's demographic dividend has gradually weakened, and the shortcomings of the manufacturing industry have begun to become prominent.In this context, the urgent needs of enterprise innovation, backward technology and production capacity changes can be changed in order to achieve a healthy development of the manufacturing industry.Select the financial data of 692 listed manufacturing companies in the A-share market from 2015 to 2019,and use a panel data model to study the internal relationship between government subsidies, R&D investment and innovation performance of listed manufacturing companies.The results show that: government subsidies and R&D investment are positively correlated with enterprise innovation performance.For manufacturing enterprises, it is necessary to improve the effect of government subsidies on the innovation performance of manufacturing enterprises through measures, such as establishing and improving the subsidy pre-investigation system, increasing subsidies, strengthening the supervision of subsidy funds, and expanding subsidy channels.By increasing the level of R&D investment, establish and improve the internal control system and formulate R&D plans to give full play to the role of government subsidies in promoting enterprise innovation performance.
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