| The digital economy is a new engine and driving force for China’s economic development.Improving the total factor productivity(TFP)of the digital economy industry is a dynamic source to promote the sustainable development of China’s digital economy.At the same time,affected by the heterogeneity of geographical location,resource endowment and institutional environment,regional differences of TFP in the digital economy industry gradually become a key concern in the process of digital economy development.A comprehensive grasp of TFP in the digital economy industry and its regional difference characteristics will provide new perspectives and new ideas for the development of the digital economy and its regional coordination in China.At present,a few studies have analyzed the dynamic evolution characteristics of TFP in China’s digital economy industries using the traditional DEA-Malmquist index method.However,there are relatively insufficient studies on the regional differences of TFP in the digital economy industry.Moreover,our study finds that the traditional DEA-Malmquist index method may have the shortcomings that it cannot effectively analyze unbalanced panel data and input missing data from smaller or larger units.Then,how can we measure the TFP of China’s digital economy industry more effectively? Are there regional differences in TFP of China’s digital economy industry? If regional differences exist,how large are they? Does it tend to converge or diverge over time? What factors contribute to this difference? How strong are the effects of different factors? An effective interpretation of above questions will help to promote the coordinated regional development of China’s digital economy.Therefore,this paper has investigated the above questions by examining the inter-provincial digital economy industry of China from 2003 to 2017.Firstly,the relevant concepts,theoretical foundations and existing classical literature involved in this paper are reviewed.Second,the traditional DEA-Malmquist index method is modified from the perspective of frontier surface correction.Third,the modified DEA-Malmquist index method was used to measure the TFP index of China’s digital economy industry effectively.Fourth,the degree of regional differences in the TFP index of China’s digital economy industry was measured by the Dagum Gini coefficient.Fifth,the convergence of regional differences in the TFP index of China’s digital economy industries was tested by the traditional three tests that σ convergence,absolute β convergence and conditional β convergence.Finally,the causes of regional differences in the TFP index of China’s digital economy industry were revealed by the variance decomposition method,quadratic assignment procedure(QAP)and geographical detector.The details and conclusions are:(1)Three methods for modifying the frontier of unbalanced panel data,such as expanding the production frontier,forecasting missing data and alternative evaluation reference sets,are proposed,and used to construct a modified DEA-Malmquist index method for analyzing TFP of unbalanced panel data.(2)A technique of frontier surface restoration based on the technological progress index is given,and used to construct a modified DEA-Malmquist index method to solve the problem of measurement bias and no feasible solution of TFP index with input missing data from smaller or larger units.(3)The TFP index of China’s digital economy industry was effectively measured by the modified DEA-Malmquist index method.The results show that: At the national level,the TFP of digital economy industries show an "N"-shaped evolution of rising,then falling,and then rising again.At the regional level,the TFP growth of digital economy industries was largest in the western region,followed by the central region and the smallest in the eastern region.At the inter-provincial level,there are some differences in the TFP growth of digital economy industries in different provinces.In addition,technological progress is the main source of TFP growth in China’s digital economy industries.(4)The degree of regional differences of TFP index in China’s digital economy industry was measured by the Dagum Gini coefficient.The results show that: In terms of overall differences,the TFP index of China’s digital economy industries show a trend of narrowing differences,and the hyper-variance density makes the largest contribution to the overall differences.In terms of intra-regional differences,provinces within the western region have the largest differences of TFP index in digital economy industries and show a significant expansion trend.In terms of inter-regional differences,TFP index of the digital economy industries has the most difference between the central and western regions,and has a relatively obvious trend of expansion.(5)The convergence trend of regional differences of TFP index in China’s digital economy industry was examined by the convergence test.The results show that: In terms of σ convergence,there is no significant convergence trend in TFP index of the digital economy industries among the whole country and in the three major regions such as east,central and west.In terms of absolute β convergence,there is a significant convergence trend in TFP index of the digital economy industries only for the whole country and the eastern region.In terms of conditional β convergence,there is a significant convergence trend in TFP index of the digital economy industries among the whole country and in the three major regions such as east,central and west.(6)The causes of regional differences in TFP index of China’s digital economy industry and the intensity of their effects are revealed using the variance decomposition method,QAP and geographical detector.The results show that:differences in pure technical efficiency changes and differences in technology introduction factor are the main determinants of regional differences in TFP index of digital economy industries in China.The interactions among different factors are all greater than their individual effects,possessing a greater intensity of influence.The main contributions of this paper are:(1)Propose three frontier repair techniques such as expanding the production frontier,forecasting missing data and alternative evaluation reference set,and which solved the shortcomings of applying the traditional DEA-Malmquist index method cannot measure the TFP index of unbalanced panel data.(2)Give a frontier surface restoration technique based on the technological progress index,and which modified the problem of measurement bias and no feasible solution when applying the traditional DEA-Malmquist index method to measure the TFP index with input missing data from smaller or larger units.(3)The Dagum Gini coefficient is applied to the analysis of regional differences in TFP of the digital economy industries in China,which not only quantifies the degree of regional differences in TFP index of digital economy industries and its sources,but also expands the scope of research on TFP of digital economy industries.(4)Incorporating the convergence theory into the analysis of regional differences in TFP of China’s digital economy industry not only analyzes the dynamic evolution trend of regional differences in TFP index of China’s digital economy industry,but also expands the application scope of neoclassical economic convergence theory.(5)Using the variance decomposition method,QAP and geographical detector,we identify the causes of regional differences in TFP of the digital economy industry in China and make up for the deficiency of research on the causes of regional differences in TFP of the digital economy industry. |
