Information
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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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 such as 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 PCA dimensionality reduction and reconstruction.In the research process,first,PCA 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 PCA inverse transformation is applied to realize interval reconstruction,which fundamentally avoids pseudo-interval generation.Finally,for multivariate scenarios,it completes modeling through one-time information integration,significantly reducing error accumulation caused by repeated data use.Meanwhile,Pseudo-interval 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.
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 and high-dimensional mixed-frequency data,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-Aresinh distribution and its combination with the skew-t distribution significantly improves the real-time prediction accuracy of economic growth risks compared to traditional Ga R methods.This improvement is achieved through integration of highdimensional 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.
Research on the Measurement,Driving Factors and Spatial Effects of the Integration Development Level of Artificial Intelligence and Digital Economy
LI Liqing;ZHONG Guangjin;The digital economy is a new form of economy that has emerged following the agricultural and industrial economies,while technological breakthroughs represented by artificial intelligence have become the driving force of the Fourth Industrial Revolution and the core engine for promoting high-quality regional economic development.It is evident that the integration of artificial intelligence and the digital economy holds significant importance in accelerating the development of an intelligent economy.Onthe basis,after analyzing the possibilities and feasibility of the integration of artificial intelligence and the digital economy,this study uses data of artificial intelligence and the digital economy from 30 Chinese provinces from 2012 to 2022,applying the entropy method and coupling coordination degree model to measure the level and degree of integration between artificial intelligence and the digital economy.Using the Dagum Gini coefficient and kernel density estimation to analyze the spatio-temporal evolution characteristics of the integration level.And employing geographic detectors and Moran's I to explore the influencing factors and spatial correlations of the integration level.The results show that the integration level of artificial intelligence and the digital economy nationwide,as well as in the three major regions,is steadily rising,indicating that the industries of artificial intelligence and the digital economy receive high government attention and are key targets of industrial policy,with policies achieving tangible results.However,the integration level exhibits distinct “high in the east,low in the west” and “economically dependent” distribution characteristics,with the eastern regions displaying both high levels and high growth rates of coupling coordination.Provinces with higher integration levels are mostly located in the eastern and some central regions,while provinces with lower integration levels are mainly in the western regions.The integration grade gradually improves,shifting from low-level to mid-level coordination.Both absolute and relative differences in integration levels continue to expand,making spatial differences increasingly pronounced,with interregional differences being the main source of spatial disparity.Industrial scale,knowledge creation,infrastructure,and innovation capacity significantly affect the improvement of the integration level,with the interaction between AI patents and industrial robots being a key focus for advancing integration to a higher level.The integration level also exhibits significant positive spatial correlation,with most regions mainly distributed in the first and third quadrants,where regions with higher(or lower) coupling coordination are more prone to clustering.Research on the integration of artificial intelligence and the digital economy provides theoretical support for the formulation of differentiated regional AI policies by the state and promotes coordinated development of technology and the economy.To further promote higher-level integration of artificial intelligence and the digital economy,it is necessary in the future to strengthen coordinated regional development to narrow the integration gap,enhance innovation-driven efforts to improve the core competitiveness of such integration,and improve digital infrastructure to solidify the foundation for the development of integrated AI and digital economy.
Statistical Measurement of Regional Digital Industrial Chain Resilience in China
MA Dan;CHEN Sinian;SHANG Sirong;CAO Runmin;Enhancing industrial chains resilience holds significant strategic importance for building a modern industrial system and achieving industrial chain modernization,serving as a crucial component in advancing China's modernization drive.Drawing on provincial-level inter-regional input-output tables for30 provincial-level administrative units in China spanning 2007 to 2023,this study constructs digital industrial chain networks by integrating input-output analysis with complex network theory,measuring resilience from two dimensions of damage resistance and recoverability,and systematically examines the characteristic patterns and influencing factors of China's digital industrial chain resilience.The results demonstrate an upward trend in China's digital industrial chain resilience,with core indicators rising continuously between 2007 and 2019 before declining following external shocks after 2019.Regional resilience levels exhibit a tiered distribution pattern descending from eastern to central,northeastern,and western regions.Further analysis reveals that the integration of digital and real economies can significantly enhance digital industrial chain resilience,showing distinct heterogeneity across different types of digital economy sectors and geographic regions.These findings suggest that it is imperative to deepen the integration of digital and real economies,optimize regional digital industry collaboration layouts,improve industry-academia-research integration and cross-sector collaboration mechanisms,and strengthen the risk resilience and self-reliance capabilities of digital industrial chains.
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 through which green finance impacts the low-carbon transformation of energy enterprises exhibit significant heterogeneity.This study employs text recognition technology based on natural language processing 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 promotes the low-carbon transformation of energy enterprises.The findings indicate that green finance serves as a crucial driver for promoting green transformation of 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.Furthermore,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 regulatory 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 of energy enterprises and advance the modernization of the energy sector.
The Employment Effect of Government-Guided Funds:From the Perspective of Enhancing Quality and Expanding Scale
FU Xiangdong;JI Yunyang;Employment is the foundation of people's livelihood and is related to the high-quality development of the economy and society as well as the long-term stability of the country.Based on the panel data matching Chinese government-guided funds with A-share listed companies in Shanghai and Shenzhen from 2009 to 2023,the impact of government-guided funds on employment scale and high-quality full employment was systematically examined.This study has found that government-guided funds can significantly increase the scale of labor employment in the invested enterprises,and this conclusion still holds true after relevant robustness tests.Mechanism analysis indicates that government-guided funds mainly increase the scale of labor employment in enterprises by empowering them to enhance the resilience of their supply chains and reduce the risk of stock price crashes.Heterogeneity analysis reveals that this employment growth effect is more prominent in regions with a low degree of marketization,non-laborintensive industries,and enterprises with a low proportion of patient capital.Further research has found that government-guided funds have enhanced the employment quality of enterprises,achieving "quality improvement and capacity expansion" in employment.Meanwhile,government-guided funds have also generated significant employment spillover effects,especially promoting the expansion of formal employment scale at the regional level.This study reveals the mechanism by which government-guided funds promote employment by stabilizing enterprise products and expectations in the capital market.It holds significant implications for systematically evaluating the policy effectiveness of government-guided funds and their role in stabilizing employment and improving people's well-being.
[Downloads: 118 ] [Citations: 0 ] [Reads: 3 ] HTML PDF Cite this article
The Impact of Artificial Intelligence on the Enterprise Carbon Emissions Marginal Abatement Cost
ZHOU Yuanxiang;CHEN Chan;XU Shuqi;ZHU Qingyuan;Artificial intelligence has become a key driver in the latest wave of technological advancements,offering new avenues for achieving China' s "dual carbon" goals while unlocking the potential of the "intelligent dividend." Against this research background,this paper adopts the cutting-edge by-product method to construct a data envelopment model,meticulously estimating the marginal carbon emission reduction costs at the enterprise level.Based on panel data of Chinese listed companies from 2013 to 2023,an "energy-saving,consumption-reducing,and efficiency-enhancing oriented" lexicon is constructed to measure firms' artificial intelligence levels,and the impact of artificial intelligence on marginal abatement costs and its mechanisms are empirically examined.The findings reveal that artificial intelligence significantly reduces enterprises' marginal carbon emission reduction costs,with this conclusion remaining robust after a series of endogeneity and robustness tests.Further mechanism analysis shows that artificial intelligence primarily affects marginal reduction costs through three pathways:fostering green technological innovation,promoting energy structure transformation,and lowering carbon emission intensity.Meanwhile,government environmental governance and market competition levels have a promoting effect on their emission reduction effects.Heterogeneity analysis further demonstrates that artificial intelligence notably lowers the marginal carbon emission reduction costs for enterprises in nonlabor-intensive,non-capital-intensive,non-high-energy-consuming industries,and high and new technology industry,with particularly significant effects in state-owned enterprises,large enterprises,and those in eastern China.Based on this,it is recommended that the government increase fiscal support for intelligent emission reduction technologies and green innovation,improve environmental protection regulations and supervision standards,maintain a fair market competition order,implement differentiated emission reduction policies based on industry characteristics,and promote coordinated emission reduction.
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Artificial Intelligence Application in Focal Firms and Green Collaborative Innovation in Supply Chains:A Green Knowledge Spillover Perspective
XIANG Shujian;XUAN Yuanyuan;Supply chain green innovation has long been constrained by structural bottlenecks in green knowledge spillovers, while the development and application of artificial intelligence technology is becoming an important pathway to break down knowledge flow barriers and reshape green collaborative innovation.From the perspective of green knowledge spillovers,this study employs a double machine learning approach to investigate the driving effect,underlying mechanism,and boundary conditions of core firms ' artificial intelligence application on supply chain green collaborative innovation.The findings reveal that core firms' artificial intelligence application significantly promotes supply chain green collaborative innovation and generates an asymmetric impact on upstream and downstream firms.Specifically,the impact on downstream firms is primarily driven by business dependency,whereas the impact on upstream firms is significantly constrained by geographic distance.Mechanism analysis shows that artificial intelligence functions mainly through two pathways:green technology knowledge spillover and green management knowledge spillover.The technology knowledge spillover exhibits a pronounced upstream-oriented characteristic,while the management knowledge spillover displays a bidirectional diffusion trend.Regarding boundary conditions,core firms' structural holes and degree centrality in the supply chain network exert differential moderating effects on green knowledge spillovers.Structural holes mainly enhance the technology knowledge spillover effect,whereas degree centrality significantly strengthens both technology and management knowledge spillovers.Therefore,it is essential to strengthen the artificial intelligence empowerment of core firms,promote knowledge spillovers in a differentiated manner, and leverage network positional advantages to break the "knowledge silos" in supply chains,thereby driving upstream and downstream green collaborative innovation.
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Research on the "Quantitative and Qualitative Improvement" Effect of National New Generation Artificial Intelligence Innovation and Development Pilot Zones on Urban Innovation
HAO Zhenlong;REN Xiumei;ZHOU Yanli;As the core driving force leading a new round of technological revolution and industrial transformation,artificial intelligence is profoundly reshaping the ecological system and development pathways of urban innovation.The difference-in-differences method is employed to systematically evaluate the impact effects,mechanisms,and heterogeneity of the National New-generation Artificial Intelligence Innovation and Development Pilot Zones on the urban innovation capacity in both quantity and quality dimensions.Findings indicate that the pilot zone policy has a positively significant effect on both the quantity and quality of urban innovation capabilities,and the conclusion remains robust after addressing endogeneity concerns and conducting a series of robustness tests.Mechanism analysis indicates that the above relationship is realized through agglomeration effects,attraction of innovative talent,and optimization of resource allocation.Heterogeneity analysis shows that the policy ' s promoting effect on innovation "quantity" is more pronounced in central cities, while its effect on innovation "quality" is more significant in eastern cities.Additionally, the policy exerts a stronger promoting effect on non-central cities than on central cities.Further analysis finds that the policy has significant spillover effects on innovation quantity for cities within 200 km and on innovation quality for cities within 100 km,and it also exhibits spillover effects on urban innovation quality at distances of 300-350 km.This research not only provides empirical evidence for a deeper understanding of the impact of the pilot zone policy but also offers policy insights for fostering innovation capacity in Chinese cities.
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The Policy Effect of Changing from Business Tax to VAT on Improving the Accuracy of Industry Classification Statistical Data
XU Yonghong;XUE Zhaoqin;JIANG An;Since 2016,China 's transition toward high-quality economic development and its pursuit of mutually beneficial international cooperation have heightened the demand for precise economic and social governance.Policymakers and scholars alike are increasingly calling for macro-statistical data that are more accurate,detailed,and aligned with international standards.However,statistical accuracy continues to be affected by industry classification practices formed during earlier periods of rapid growth, when mixed operational structures were common.A key source of inaccuracy lies in the misclassification of auxiliary service activities within enterprises.In China,auxiliary units are typically classified according to the primary industry of their parent enterprise.This practice,however,diverges from the 2008 System of National Accounts(SNA 2008),which stipulates that auxiliary units that are geographically separate or maintain independent accounts should be recorded as distinct statistical entities,even if they lack independent legal personality.This study focuses on how China's " Business Tax to Value-Added Tax Reform "(B2 V Reform)addresses this statistical discrepancy.The reform reduced the tax burden on smaller auxiliary units by shifting them from the business tax regime to the value-added tax system, distinct from their parent enterprises.This change creates a strong economic incentive for firms to legally separate their auxiliary service departments.As a result,the reform promotes more accurate industry classification,especially for diversified enterprises in sectors such as manufacturing and mining,whose core activities involve tangible goods production.Given these economic-statistical linkages,the B2 V Reform offers a valuable opportunity to evaluate the accuracy of industry classification data and to inform adjustments to historical statistical records.Using provincial panel data from 2003 to 2021,this paper applies a three-dimensional time-based analytical framework to empirically assess the impact of the B2 V Reform on the accuracy of industry classification data and to examine variations across sectors.Exploratory adjustments are also made to relevant historical data.The findings indicate that the reform significantly improved data accuracy in pilot industries during its initial phase, while its effect on other industries in later stages was more limited.Heterogeneity analysis reveals that the reform had the strongest impact on the "information transmission,software,and information technology services" industry and the "leasing and business services" industry.After historical data adj ustment, employment figures in these two corrected sectors-based on urban nonprivate units-exceed the originally reported values.This outcome confirms that the B2 V Reform helps mitigate the underestimation of historical data in related service industries.Overall,this study validates the role of the B2 V Reform in improving the accuracy of industry classification data.The findings are expected to help reduce statistical discrepancies in China ' s official practice and to provide a useful reference for the National Bureau of Statistics in revising economic statistical data.
[Downloads: 246 ] [Citations: 0 ] [Reads: 15 ] HTML PDF Cite this article
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: 135 ] 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: 24,093 ] [Citations: 922 ] [Reads: 109 ] 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: 155 ] HTML PDF Cite this article
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.
[Downloads: 14,149 ] [Citations: 551 ] [Reads: 113 ] HTML PDF Cite this article
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: 135 ] 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: 155 ] 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: 24,093 ] [Citations: 922 ] [Reads: 109 ] 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.
[Downloads: 14,989 ] [Citations: 467 ] [Reads: 328 ] HTML PDF Cite this article
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