Abstract:Profound changes have taken place in China’s regional economic layout and spatial structure. As a major form of the new-type urbanization, the development pattern of urban agglomeration is in the ascendant. Urban agglomeration has become an important carrier and platform in leading China’s economic transformation and upgrading. To enhance the economic and population carrying capacity of the central cities and urban agglomeration, and to promote their roles as radiators and power-houses are the key to regional coordinated development during the 14th Five-Year Plan period. In recent years, with the boom in digital economy, the central and local governments have paid more and more attention to the development of digital economy. Whether the development of digital economy can strengthen the leading role of urban agglomeration and central cities and promote the coordinated development of regional economy is of great significance for the establishment of a “dual circulation” development pattern. Meanwhile, it is also an academic and practical topic that deserves in-depth research. Taking cities at prefecture level and above among China’s three largest urban agglomerations, the Beijing-Tianjin-Hebei agglomeration (BTHA), the Yangtze River Delta agglomeration (YRDA), and the Pearl River Delta agglomeration (PRDA) as research samples, this paper empirically tests the impact of digital economy on the coordinated development of urban agglomeration by using Spatial Dubin Model (SDM) and Panel Instrumental Variable Model.The innovation and marginal contributions of this paper are reflected in the following two aspects. First, perspective innovation. The paper evaluates the spatial impact of digital economy on the urban agglomeration based on empirical experience, clearly gives answers to whether digital economy can integrate multiple forces of factors, production and consumption, remove segmentation between cities caused by administrative division, and promote the effective expansion of urban agglomeration boundaries. In doing so, it not only expands the perspective of digital economy impact assessment but also provides useful ideas for effectively giving play to the leading role of urban agglomeration so as to compensate for the lack of spatial perspective in the current research of digital economy, which reflects the research depth to a certain extent. Second, method innovation. This paper employs the latest NPP-VIIRS global night-time light data to identify urban economic activities, and take the amount of annual news information in closely related entries of “digital economy” obtained by keyword retrieval and crawling on the Baidu search engine as a measure of the development of digital economy in cities. In this way, it not only expands the sample size but also effectively avoids the potential endogenous problems in the empirical research, which helps eliminate interference of some other factors. Therefore, this new method is helpful to test the causal effect of digital economy and urban agglomeration coordinated development.The main conclusions of this paper are as follows. Firstly, in the era of digital economy, the pace of regional economic development driven by urban agglomeration is accelerating. The most prominent feature is that the economic effects brought about by urban agglomeration begin to spread to non-urban agglomeration areas. Further observation of the geographical radius of urban agglomeration digital economy spillover shows that there is a dense area of digital economy spatial spillover within 200 km, and the spillover effect gradually decreases from about 300 km. Overall, the average distance of spatial spillover of digital economy is about 250 km.Secondly, there are two ways for the digital economy to promote the coordinated development of urban agglomeration. First, by means of the patent cooperation data at the micro level, it is revealed that digital economy can promote trans-city and trans-regional cooperation and innovation of innovative subjects, and an urban collaborative innovation network is forming, which enables closer connections between cities. Secondly, by improving urban spatial concentration, digital economy has made cities more attractive to gather surrounding population and industries, and the urban population agglomeration has increased within the adjacent space.Thirdly, in an era of digital economy, the central city plays a more important role. Specifically, the digital economy of central cities has a strong spatial spillover effect on the peripheral cities. Besides, central cities will have larger effects as the radiators and power-houses of their peripheral cities as digital economy connects different regions like a network, breaks geographical barriers and provides externalities shared by these regions. These findings provide direct evidences for further giving play to the role of digital economy as an impetus and strengthening the leading role of central cities and urban agglomeration in high-quality economic development.
邓慧慧, 周梦雯, 程钰娇. 数字经济与城市群协同发展:基于夜间灯光数据的研究[J]. 浙江大学学报(人文社会科学版), 2022, 52(4): 32-49.
Deng Huihui, Zhou Mengwen, Cheng Yujiao. Digital Economy and Urban Agglomeration Coordinated Development: A Research Based on Night-Time Light Data. JOURNAL OF ZHEJIANG UNIVERSITY, 2022, 52(4): 32-49.
安同良、杨晨: 《互联网重塑中国经济地理格局:微观机制与宏观效应》,《经济研究》2020年第2期,第4-19页。2 王军、朱杰、罗茜: 《中国数字经济发展水平及演变测度》,《数量经济技术经济研究》2021年第7期,第26-42页。3 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》,《管理世界》2020年第10期,第65-76页。4 杨慧梅、江璐: 《数字经济、空间效应与全要素生产率》,《统计研究》2021年第4期,第3-15页。5 钱海章、陶云清、曹松威等: 《中国数字金融发展与经济增长的理论与实证》,《数量经济技术经济研究》2020年第6期,第26-46页。6 Lin J., Yu Z. & Wei Y. D. et al., “Internet access, spillover and regional development in China,” Sustainability,Vol. 9, No. 6 (2017), pp. 1-18.7 荆文君、孙宝文: 《数字经济促进经济高质量发展:一个理论分析框架》,《经济学家》2019年第2期,第66-73页。8 黄群慧、余泳泽、张松林: 《互联网发展与制造业生产率提升:内在机制与中国经验》,《中国工业经济》2019年第8期,第5-23页。9 戚聿东、肖旭: 《数字经济时代的企业管理变革》,《管理世界》2020年第6期,第135-152页。10 赵宸宇、王文春、李雪松: 《数字化转型如何影响企业全要素生产率》,《财贸经济》2021年第7期,第114-129页。11 Kuhn P. & Skuterud M., “Internet job search and unemployment durations,” American Economic Review, Vol. 94, No. 1 (2004), pp. 218-232.12 罗珉、李亮宇: 《互联网时代的商业模式创新:价值创造视角》,《中国工业经济》2015年第1期,第95-107页。13 郭家堂、骆品亮: 《互联网对中国全要素生产率有促进作用吗?》,《管理世界》2016年第10期,第34-49页。14 陶云清、曹雨阳、张金林等: 《数字金融对创业的影响——来自地区和中国家庭追踪调查(CFPS)的证据》,《浙江大学学报(人文社会科学版)》2021年第1期,第129-144页。15 Zacchia P., “Knowledge spillovers through networks of scientists,” The Review of Economic Studies, Vol. 87, No. 4 (2020), pp. 1989-2018.16 原倩: 《城市群是否能够促进城市发展》,《世界经济》2016年第9期,第99-123页。17 Krugman P. R., “Making sense of the competitiveness debate,” Oxford Review of Economic Policy, Vol. 12, No. 3 (1996), pp. 17-25.18 王雨飞、倪鹏飞、赵佳涵等: 《交通距离、通勤频率与企业创新——高铁开通后与中心城市空间关联视角》,《财贸经济》2021年第12期,第150-165页。19 陆铭、向宽虎: 《地理与服务业——内需是否会使城市体系分散化?》,《经济学(季刊)》2012年第3期,第1079-1096页。20 王贤彬、吴子谦: 《城市群中心城市驱动外围城市经济增长》,《产业经济评论》2018年第3期,第54-71页。21 施炳展、金祥义: 《注意力配置、互联网搜索与国际贸易》,《经济研究》2019年第11期,第71-86页。22 李春涛、闫续文、宋敏等: 《金融科技与企业创新——新三板上市公司的证据》,《中国工业经济》2020年第1期,第81-98页。23 Elvidge C. D., Cinzano P. & Pettit D. R. et al., “The nightsat mission concept,” International Journal of Remote Sensing, Vol. 28, No. 12 (2007), pp. 2645-2670.24 Elvidge C. D., Sutton P. C. & Ghosh T. et al., “A global poverty map derived from satellite data,” Computers & Geosciences, Vol. 35, No. 8 (2009), pp. 1652-1660.25 Chen X. & Nordhaus W. D., “Using luminosity data as a proxy for economic statistics,” Proceedings of the National Academy of Sciences, Vol. 108, No. 21 (2011), pp. 8589-8594.26 Henderson J. V., Storeygard A. & Weil D. N., “Measuring economic growth from outer space,” American Economic Review, Vol. 102, No. 2 (2012), pp. 994-1028.27 Donaldson D. & Storeygard A., “The view from above: applications of satellite data in economics,” Journal of Economic Perspectives, Vol. 30, No. 4 (2016), pp. 171-198.28 Michalopoulos S. & Papaioannou E., “National institutions and subnational development in Africa,” The Quarterly Journal of Economics, Vol. 129, No. 1 (2014), pp. 151-213.29 Elliott R. J., Strobl E. & Sun P., “The local impact of typhoons on economic activity in China: a view from outer space,” Journal of Urban Economics, Vol. 88 (2015), pp. 50-66.30 范子英、彭飞、刘冲: 《政治关联与经济增长——基于卫星灯光数据的研究》,《经济研究》2016年第1期,第114-126页。31 秦蒙、刘修岩: 《城市蔓延是否带来了我国城市生产效率的损失?——基于夜间灯光数据的实证研究》,《财经研究》2015年第7期,第28-40页。32 刘修岩、李松林、秦蒙: 《开发时滞、市场不确定性与城市蔓延》,《经济研究》2016年第8期,第159-171页。33 刘修岩、李松林、秦蒙: 《城市空间结构与地区经济效率——兼论中国城镇化发展道路的模式选择》,《管理世界》2017年第1期,第51-64页。34 Elvidge C. D., Baugh K. & Zhizhin M. et al., “VIIRS night-time lights,” International Journal of Remote Sensing, Vol. 38, No. 21 (2017), pp. 5860-5879.35 Nunn N. & Qian N., “US food aid and civil conflict,” American Economic Review, Vol. 104, No. 6 (2014), pp. 1630-1666.36 王贤彬、黄亮雄、徐现祥等: 《中国地区经济差距动态趋势重估——基于卫星灯光数据的考察》,《经济学(季刊)》2017年第3期,第877-896页。37 陈梦根、张帅: 《中国地区经济发展不平衡及影响因素研究——基于夜间灯光数据》,《统计研究》2020年第6期,第40-54页。38 Portnov B. A. & Schwartz M., “Urban clusters as growth foci,” Journal of Regional Science, Vol. 49, No. 2 (2009), pp. 287-310.39 覃成林、杨霞: 《先富地区带动了其他地区共同富裕吗——基于空间外溢效应的分析》,《中国工业经济》2017年第10期,第44-61页。
1
安同良、杨晨: 《互联网重塑中国经济地理格局:微观机制与宏观效应》,《经济研究》2020年第2期,第4-19页。2 王军、朱杰、罗茜: 《中国数字经济发展水平及演变测度》,《数量经济技术经济研究》2021年第7期,第26-42页。3 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》,《管理世界》2020年第10期,第65-76页。4 杨慧梅、江璐: 《数字经济、空间效应与全要素生产率》,《统计研究》2021年第4期,第3-15页。5 钱海章、陶云清、曹松威等: 《中国数字金融发展与经济增长的理论与实证》,《数量经济技术经济研究》2020年第6期,第26-46页。6 Lin J., Yu Z. & Wei Y. D. et al., “Internet access, spillover and regional development in China,” Sustainability,Vol. 9, No. 6 (2017), pp. 1-18.7 荆文君、孙宝文: 《数字经济促进经济高质量发展:一个理论分析框架》,《经济学家》2019年第2期,第66-73页。8 黄群慧、余泳泽、张松林: 《互联网发展与制造业生产率提升:内在机制与中国经验》,《中国工业经济》2019年第8期,第5-23页。9 戚聿东、肖旭: 《数字经济时代的企业管理变革》,《管理世界》2020年第6期,第135-152页。10 赵宸宇、王文春、李雪松: 《数字化转型如何影响企业全要素生产率》,《财贸经济》2021年第7期,第114-129页。11 Kuhn P. & Skuterud M., “Internet job search and unemployment durations,” American Economic Review, Vol. 94, No. 1 (2004), pp. 218-232.12 罗珉、李亮宇: 《互联网时代的商业模式创新:价值创造视角》,《中国工业经济》2015年第1期,第95-107页。13 郭家堂、骆品亮: 《互联网对中国全要素生产率有促进作用吗?》,《管理世界》2016年第10期,第34-49页。14 陶云清、曹雨阳、张金林等: 《数字金融对创业的影响——来自地区和中国家庭追踪调查(CFPS)的证据》,《浙江大学学报(人文社会科学版)》2021年第1期,第129-144页。15 Zacchia P., “Knowledge spillovers through networks of scientists,” The Review of Economic Studies, Vol. 87, No. 4 (2020), pp. 1989-2018.16 原倩: 《城市群是否能够促进城市发展》,《世界经济》2016年第9期,第99-123页。17 Krugman P. R., “Making sense of the competitiveness debate,” Oxford Review of Economic Policy, Vol. 12, No. 3 (1996), pp. 17-25.18 王雨飞、倪鹏飞、赵佳涵等: 《交通距离、通勤频率与企业创新——高铁开通后与中心城市空间关联视角》,《财贸经济》2021年第12期,第150-165页。19 陆铭、向宽虎: 《地理与服务业——内需是否会使城市体系分散化?》,《经济学(季刊)》2012年第3期,第1079-1096页。20 王贤彬、吴子谦: 《城市群中心城市驱动外围城市经济增长》,《产业经济评论》2018年第3期,第54-71页。21 施炳展、金祥义: 《注意力配置、互联网搜索与国际贸易》,《经济研究》2019年第11期,第71-86页。22 李春涛、闫续文、宋敏等: 《金融科技与企业创新——新三板上市公司的证据》,《中国工业经济》2020年第1期,第81-98页。23 Elvidge C. D., Cinzano P. & Pettit D. R. et al., “The nightsat mission concept,” International Journal of Remote Sensing, Vol. 28, No. 12 (2007), pp. 2645-2670.24 Elvidge C. D., Sutton P. C. & Ghosh T. et al., “A global poverty map derived from satellite data,” Computers & Geosciences, Vol. 35, No. 8 (2009), pp. 1652-1660.25 Chen X. & Nordhaus W. D., “Using luminosity data as a proxy for economic statistics,” Proceedings of the National Academy of Sciences, Vol. 108, No. 21 (2011), pp. 8589-8594.26 Henderson J. V., Storeygard A. & Weil D. N., “Measuring economic growth from outer space,” American Economic Review, Vol. 102, No. 2 (2012), pp. 994-1028.27 Donaldson D. & Storeygard A., “The view from above: applications of satellite data in economics,” Journal of Economic Perspectives, Vol. 30, No. 4 (2016), pp. 171-198.28 Michalopoulos S. & Papaioannou E., “National institutions and subnational development in Africa,” The Quarterly Journal of Economics, Vol. 129, No. 1 (2014), pp. 151-213.29 Elliott R. J., Strobl E. & Sun P., “The local impact of typhoons on economic activity in China: a view from outer space,” Journal of Urban Economics, Vol. 88 (2015), pp. 50-66.30 范子英、彭飞、刘冲: 《政治关联与经济增长——基于卫星灯光数据的研究》,《经济研究》2016年第1期,第114-126页。31 秦蒙、刘修岩: 《城市蔓延是否带来了我国城市生产效率的损失?——基于夜间灯光数据的实证研究》,《财经研究》2015年第7期,第28-40页。32 刘修岩、李松林、秦蒙: 《开发时滞、市场不确定性与城市蔓延》,《经济研究》2016年第8期,第159-171页。33 刘修岩、李松林、秦蒙: 《城市空间结构与地区经济效率——兼论中国城镇化发展道路的模式选择》,《管理世界》2017年第1期,第51-64页。34 Elvidge C. D., Baugh K. & Zhizhin M. et al., “VIIRS night-time lights,” International Journal of Remote Sensing, Vol. 38, No. 21 (2017), pp. 5860-5879.35 Nunn N. & Qian N., “US food aid and civil conflict,” American Economic Review, Vol. 104, No. 6 (2014), pp. 1630-1666.36 王贤彬、黄亮雄、徐现祥等: 《中国地区经济差距动态趋势重估——基于卫星灯光数据的考察》,《经济学(季刊)》2017年第3期,第877-896页。37 陈梦根、张帅: 《中国地区经济发展不平衡及影响因素研究——基于夜间灯光数据》,《统计研究》2020年第6期,第40-54页。38 Portnov B. A. & Schwartz M., “Urban clusters as growth foci,” Journal of Regional Science, Vol. 49, No. 2 (2009), pp. 287-310.39 覃成林、杨霞: 《先富地区带动了其他地区共同富裕吗——基于空间外溢效应的分析》,《中国工业经济》2017年第10期,第44-61页。