Skip to main content
Log in

Online inter-provincial trade in China

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

There is a large disparity in terms of per capital e-commerce consumption between more developed east coastal provinces and less developed inner provinces in China, which reflects the disparities in economic development and income between these regions. E-commerce could integrate China’s domestic economy as it facilitates cross-region flow of goods, strengthens regional linkage, and becomes the channel through which economic growth in one region spills over to other regions. On the other hand, e-commerce could also make regions more divergent. With a reduced trading cost, regions with comparative advantages in certain industries could have even wider and farther reaches and enjoy increasing returns. In this study, we specifically examine the pattern of e-commerce activities in China over time. We find that the production and sales of agricultural and clothing products have become more concentrated over time. The trade pattern as measured by trade flows among different provinces is mixed. While the pattern is slightly more concentrated in B2C platform, the pattern is more diversified for C2C market. Our regression analysis indicates that GDPs in origin provinces positively affect the trade flows, but distances impede trade, consistent with the gravity model. Interestingly, GDPs of the destination provinces are not significant. Home market effect is also supported.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. National Development and Reform Commission, The 11th 5-year plan of electronic commerce development, 2007.

  2. http://data.stats.gov.cn/.

  3. http://www.chinahighway.gov.cn/.

References

  • Anand K, Bianconi G (2009) Entropy measures for networks: toward an information theory of complex topologies. Phys Rev E 80:045102

    Article  Google Scholar 

  • Anderson JE, Van Wincoop E (2001) Gravity with gravitas: a solution to the border puzzle. National Bureau of economic research

  • Bai C-E, Ma H, Pan W (2012) Spatial spillover and regional economic growth in China. China Econ Rev 23:982–990

    Article  Google Scholar 

  • Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  • Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci USA 101:3747–3752

    Article  Google Scholar 

  • Brakman S, Garretsen H, Van Marrewijk C (2009) Economic geography within and between European nations: the role of market potential and density across space and time. J Reg Sci 49:777–800

    Article  Google Scholar 

  • Candelaria C, Daly M, Hale G (2015) Persistence of regional wage differences in China. Pac Econ Rev 20:365–387

    Article  Google Scholar 

  • Chern C-C, Wei C-P, Shen F-Y, Fan Y-N (2015) A sales forecasting model for consumer products based on the influence of online word-of-mouth. Inf Syst E-Bus Manag 13:445–473

    Article  Google Scholar 

  • Corden W (1970) A note on economies of scale, the size of the domestic market and the pattern of trade. In: McDougall IA, Snape RH (eds) Studies in international economics. North-Holland, Amsterdam

    Google Scholar 

  • Deardorff AV (1984) Testing trade theories and predicting trade flows. In: Jones R, Kenen P (eds) Handbook of international economics. North-Holland, Amsterdam

    Google Scholar 

  • Eagle N, Macy M, Claxton R (2010) Network diversity and economic development. Science 328:1029–1031. doi:10.1126/science.1186605

    Article  Google Scholar 

  • Gómez E, Martens B, Turlea G (2012) The drivers and impediments for online cross-border trade in goods in the EU. Digital economy research programme working paper

  • Krugman P (1980) Scale economies, product differentiation, and the pattern of trade. Am Eco Rev 70:950–959

    Google Scholar 

  • Lendle A, Schropp S, Olarreaga M, Vezina P-L (2012) There goes gravity: how eBay reduces trade costs. From CEPR Discussion Paper No. DP9094. https://ssrn.com/abstract=2153544

  • Liljeros F, Edling CR, Amaral LAN, Stanley HE, Åberg Y (2001) The web of human sexual contacts. Nature 411:907–908

    Article  Google Scholar 

  • Newman ME (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  Google Scholar 

  • Serrano MÁ, Boguñá M (2003) Topology of the world trade web. Phys Rev E 68:015101

    Article  Google Scholar 

  • Wimble M, Tripp J, Phillps B, Milic N (2016) On search cost and the long tail: the moderating role of search cost. Inf Syst E-Bus Manage 14:507–531

    Google Scholar 

  • Wolf HC (2000) Intranational home bias in trade. Rev Econ Stat 82:555–563

    Article  Google Scholar 

Download references

Acknowledgements

The first two authors would like to thank the support from the National Social Science Foundation of China (Grant Number 14ZDB137).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Liu, Y. & Fan, M. Online inter-provincial trade in China. Inf Syst E-Bus Manage 16, 831–842 (2018). https://doi.org/10.1007/s10257-017-0348-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-017-0348-9

Keywords

Navigation