数字经济产业发展对于推动中国经济新旧动能转换和质量变革具有重要的意义。文章基于数字经济相关企业大数据,采用核密度估计方法测算了2000—2020年中国285个地级及以上城市数字经济产业发展水平,分析了数字经济产业的时空分异格局及演化特征,进一步运用Dagum基尼系数法揭示其区域差异,并使用σ收敛模型和β收敛模型考察其收敛性。研究发现：①中国数字经济产业发展规模呈“U”型发展趋势,全国数字经济产业呈现“城市群带动、沿海大集聚—内陆小集聚”的空间格局。集聚热点表现为“东强西弱,南热北冷”,各城市群呈现“多核心集聚”的空间格局,沿海城市群连片集聚,内陆城市群离散集聚。②全国层面数字经济产业发展差异存在加速收窄特征,东中西部视角下,差异来源主要为区域间差距的贡献,南北方视角下,差异来源主要为区域内差距的贡献。除中—西部地区之间的差距近年来有所上升外,区域间差距总体趋于下降。③全国及各区域存在σ收敛、绝对与条件β收敛特征,且条件β收敛下各区域收敛速度更快,西部地区不存在σ收敛现象。
The development of the digital economy industry is of great significance to promoting the transition of China's economy and its quality reform. Based on the big data of enterprises' location related to the digital economy,this paper applies the kernel density estimation method to measure the development level of digital economy industry in 285 cities at prefecture level and above in China from 2000 to 2020,and analyzes the spatiotemporal differentiation pattern and evolution characteristics of the digital economy industry. Additionally, this paper reveals the industry's regional differences adopting the Dagum Gini coefficient method and investigates its convergence using the σ convergence model and the β convergence model. The results show that: 1) The industrial development scale of the digital economy in China shows the U-shaped development trend. The national digital economy industry shows a spatial pattern of cities-driven agglomeration,with large agglomeration in coastal cities and small agglomeration in inland cities. The agglomeration hotspots are strong in the east of China and weak in the west,hot in the south and cold in the north. Each urban agglomeration presents the spatial pattern of multi-core agglomeration. 2) The differences in the development of digital economy industries at the national level are characterized by accelerated narrowing. From the perspective of the eastern,central and western regions,the source of the difference is mainly the contribution of the inter-regional gap. From the perspective of the north and the south,the source of the difference is mainly the contribution of the intra-regional gap. In addition to the gap between the central and western regions,the gap between other regions tends to decline. 3) There are σ convergence,absolute and conditional β convergence characteristics nationally and regionally. The convergence speed of each region is faster under conditional β convergence,and there is no σ convergence phenomenon in the western region.
[1] 张勋,万广华,张佳佳,等. 数字经济、普惠金融与包容性增长[J]. 经济研究,2019,54(8):71-86.
[2] 赵涛,张智,梁上坤. 数字经济、创业活跃度与高质量发展——来自中国城市的经验证据[J]. 管理世界,2020,36(10):65-76.
[3] 田秀娟,李睿. 数字技术赋能实体经济转型发展——基于熊彼特内生增长理论的分析框架[J]. 管理世界,2022,38(5):56-74.
[4] 田鸽,张勋. 数字经济、非农就业与社会分工[J]. 管理世界,2022,38(5):72-84.
[5] 中国信息通信研究院. 中国数字经济发展报告(2022年)[R]. 2022.
[6] 裴长洪,倪江飞,李越. 数字经济的政治经济学分析[J]. 财贸经济,2018,39(9):5-22.
[7] 王开科,吴国兵,章贵军. 数字经济发展改善了生产效率吗[J]. 经济学家,2020,(10):24-34.
[8] 江小涓,罗立彬. 网络时代的服务全球化——新引擎、加速度和大国竞争力[J]. 中国社会科学,2019,(2):68-91.
[9] Baldwin R E,Martin P,Ottaviano G I P. Global income divergence,trade,and industrialization:The geography of growth take-offs[J]. Journal of Economic Growth,2001,6(1):5-37.
[10] 王军,朱杰,罗茜. 中国数字经济发展水平及演变测度[J]. 数量经济技术经济研究,2021,38(7):26-42.
[11] 潘为华,贺正楚,潘红玉. 中国数字经济发展的时空演化和分布动态[J]. 中国软科学,2021(10):137-147.
[12] 戴若尘,王艾昭,陈斌开. 中国数字经济核心产业创新创业:典型事实与指数编制[J]. 经济学动态,2022(4):29-48.
[13] 王胜鹏,滕堂伟,夏启繁,等. 中国数字经济发展水平时空特征及其创新驱动机制[J]. 经济地理,2022,42(7):33-43.
[14] 李研. 中国数字经济产出效率的地区差异及动态演变[J]. 数量经济技术经济研究,2021,38(2):60-77.
[15] 毛丰付,高雨晨,周灿. 长江经济带数字产业空间格局演化及驱动因素[J]. 地理研究,2022,41(6):1593-1609.
[16] 范剑勇,冯猛,李方文. 产业集聚与企业全要素生产率[J]. 世界经济,2014,37(5):51-73.
[17] 谢露露. 产业集聚和工资“俱乐部”:来自地级市制造业的经验研究[J]. 世界经济,2015,38(10):148-168.
[18] Lin J,Yu Z,Wei Y D,et al.Internet access,spillover and regional development in China[J]. Sustainability,2017,9,946,doi:10.3390/su9060946.
[19] Greve H R.An ecological theory of spatial evolution:Local density dependence in Tokyo banking,1894-1936[J]. Social Forces,2002,80(3):847-879.
[20] Tapscott D.The Digital Economy:Promise and Peril in the Age of Networked Intelligence[M]. New York:McGraw-Hill,1996.
[21] Bukht R,Heeks R.Defining,conceptualising and measuring the digital economy[R]. GDI Development Informatics Working Papers 2017 .
[22] Knickrehm M,Berthon B,Daugherty P.Digital disruption:The growth multiplier[J]. Accenture Strategy,2016(1):1-12.
[23] 中国信息通信研究院. 中国数字经济发展白皮书(2017年)[R]. 2017.
[24] 杨慧梅,江璐. 数字经济、空间效应与全要素生产率[J]. 统计研究,2021,38(4):3-15.
[25] 杨勇. 集聚密度、多样性和旅游企业劳动生产率——兼对产业聚集理论观点的拓展研究[J]. 财贸经济,2015,(2):148-161.
[26] Dagum C.Decomposition and interpretation of Gini and the generalized entropy inequality measures[J]. Statistica,1997,57(3):295-308.
[27] 杨嬛,郑秀君,张学良. 空间产业密度与企业诞生:基于长三角地区的实证研究[J]. 财经研究,2012,38(11):49-59.
[28] Silverman W.Density Estimation for Statistics and Data Analysis[M]. New York:Routledge. 1998.
[29] 詹姆斯·勒沙杰,凯利·佩斯. 空间计量经济学导论[M]. 北京:北京大学出版社,2014.
[30] 彭文斌,韩东初,尹勇,等. 京津冀地区数字经济的空间效应研究[J]. 经济地理,2022,42(5):136-143,232.
[31] 张帅,吴珍玮,陆朝阳,等. 中国省域数字经济与实体经济融合的演变特征及驱动因素[J]. 经济地理,2022,42(7):22-32.
[32] 孙勇,樊杰,刘汉初,等. 长三角地区数字技术创新时空格局及其影响因素[J]. 经济地理,2022,42(2):124-133.
[33] 张永年,潘竟虎. 基于DMSP/OLS数据的中国碳排放时空模拟与分异格局[J]. 中国环境科学,2019,39(4):1436-1446.
[34] Zhang Y,Pan J,Zhang Y,et al.Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data[J]. Journal of Geographical Sciences,2021,31(3):327-349.
[35] 孙杰. 从数字经济到数字贸易:内涵、特征、规则与影响[J]. 国际经贸探索,2020,36(5):87-98.
[36] 陈建军,胡晨光. 产业集聚的集聚效应——以长江三角洲次区域为例的理论和实证分析[J]. 管理世界,2008,(6):68-83.
