Research On Evaluation Of Innovation Efficiency And Spatial Spillover Effect Of Productive Services In China

Innovation is the main force for industrial development,and the innovative development of producer services can lay a solid foundation for a new round of innovative and upgraded development of the manufacturing industry.In the context of economic globalization,more professional division of labor in domestic and foreign industries.The spatial connection of industrial development is becoming increasingly close.The formation of a new pattern of national and regional strategic cooperation.Spatial imbalance in industrial development between regions.Innovation becomes the core driving force for the development of productive services.In addition,China is currently in a critical period of promoting high-quality economic growth and industrial upgrading and transformation.Therefore,this article selects the research on the evaluation of innovation efficiency and spatial spillover effects in China’s productive services industry as its topic.Firstly,it sorts out the background,significance,research ideas,theoretical basis,and literature review of the topic.Secondly,it deeply analyzes the theoretical analysis and research hypothesis of innovation efficiency in producer services.Then,taking the innovation efficiency of productive services in 296 prefecture-level cities in China from 2011 to 2020 as the research object.The DEA-BCC model and the Malmquist index model are used to scientifically evaluate the innovation efficiency of the producer services industry.Then,the TC efficiency value DEA model is used to measure the innovation efficiency level of producer services.Next,the measurement results of innovation efficiency of producer services are analyzed in depth,and the spatial-temporal characteristics of innovation efficiency of producer services are preliminarily analyzed using kernel density estimation.Then,it conducts an empirical test on the innovation efficiency of producer services,examining the spatial impact of municipal government funding,industrial personnel quality,economic foundation,urbanization rate and reachability of transportation on the innovation efficiency of producer services.On this basis,a threshold effect model is used to analyze the nonlinear spatial spillover effects and explore the actual distribution of spatial spillover effects.In the last resort,the research summary and policy implications and future research expectations of this article are proposed.The key findings of this article are as follows.1.The overall innovation efficiency of China’s urban productive service industry has shown a continuous improvement trend.Without considering external environmental and random factors,it can be found that the overall innovation efficiency of the productive services industry in the eight major economic zones of China’s urban economic zones showed a trend of continuous improvement and change during 2011-2020,with a small range of change and basically maintaining a stable upward trend,indicating that the development and innovation capacity of the productive services industry in the eight major economic zones is steadily improving.2.There are certain spatial and temporal differences in the innovation efficiency of urban productive services in China.From a national perspective,since 2011,China’s productive service industry has made significant breakthroughs and progress in technological innovation,and innovation efficiency has been significantly improved.Based on the Kernel density estimation results,it can be found that the innovation efficiency of producer services at the national level shows the distribution characteristics of the main peak position moving to the right,the main peak value falling after rising,and the main peak width expanding after shrinking,and there is no polarization or multipolar differentiation.From the spatial distribution pattern,it can be observed that there is a certain degree of spatial agglomeration characteristic in the innovation efficiency of productive service industries.Overall,the trend of innovation efficiency in the productive service industry in the eight major economic regions is relatively similar to that at the national level,showing a fluctuating upward trend.3.There is a significant correlation between innovation efficiency and influencing factors in producer services.The regression results of the model indicate that government funding and economic base are positively correlated with the innovation effectiveness of productive services,and the regression analysis results of the above indicators are consistent with the original hypothesis.The quality of industrial personnel,urbanization rate and traffic reachability have a significant negative correlation with the innovation efficiency of producer services,contrary to the original hypothesis.At the same time,through the endogeneity test and robustness test,it further proves that the two-way fixed effect model and variable data selected in this paper are reasonable and scientific,eliminates the endogeneity problem in the model,and further proves the robustness of the impact effect model of innovation efficiency in producer services.4.The impact of innovation efficiency on producer services is not linearly related.In order to study the non-linear relationship between the innovation efficiency of producer services,this paper constructs a threshold effect model to explore whether there is a non-linear relationship between the impact of innovation efficiency of producer services.Through model testing,it is found that when the level of economic development is used as a threshold variable,the impact of productive service industry innovation efficiency has a dual threshold effect.It is further verified that there is a non-linear relationship between the impact of productive service industry innovation efficiency.From the perspective of the threshold effect of industrial structure,its industrial structure has a strong significant effect on it.5.The spatial spillover effect on the innovation efficiency of China’s urban productive services is significant.The results of the spatial autocorrelation tests show that,given the existence of spatial spillover effects,the efficiency index of productive services sector innovation,with the exception of not being significant in 2017,passed the significance test for the overall Moran’I value of the productive services industry innovation efficiency index in the remaining years from 2011 to 2020,and it can be found through the local Moran index scatter distribution diagram.Most of the scattered plots of the local Moran index show the spatial distribution characteristics of high to high clustering and low to low clustering,which preliminarily tests the relatively obvious spatial spillover effect of the innovation efficiency index of China’s urban productive services industry in space.Based on the test of the spatial econometric model,this paper uses the SDM model to explore the spatial spillover effect of the innovation efficiency of China’s urban producer services.On the basis of testing the spatial econometric model,this article uses the SDM model to explore the spatial spillover effect of innovation efficiency in China’s urban productive service industry,which is generally significant.In summary,the improvement of innovation efficiency of producer services needs to start with capital,talent,technology and other aspects,including strengthening the government’s unified planning function,reasonably allocating R&D funds,innovating the talent management system,breaking through core technology,optimizing the innovation development environment,and forming regional linkage competitive advantages.Due to the varying levels and processes of innovative development in productive service industries across different regions,it is necessary to implement spatial differentiation development policies based on the specific circumstances of the eight major economic zones.