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A scale-free transportation network explains the city-size distribution

  • Berliant, Marcus
  • Watanabe, Hiroki

Zipf’s law is one of the best-known empirical regularities of the city-size distribution. There is extensive research on the subject, where each city is treated symmetrically in terms of the cost of transactions with other cities. Recent developments in network theory facilitate the examination of an asymmetric transport network. Under the scale-free transport network framework, the chance of observing extremes becomes higher than the Gaussian distribution predicts and therefore it explains the emergence of large clusters. City-size distributions share the same pattern. This paper proposes a way to incorporate network structure into urban economic models and explains the city-size distribution as a result of transport cost between cities.

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File URL: http://mpra.ub.uni-muenchen.de/34820/1/MPRA_paper_34820.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 34820.

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Date of creation: 17 Nov 2011
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Handle: RePEc:pra:mprapa:34820
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  1. Berliant, Marcus & Watanabe, Hiroki, 2009. "Explaining the size distribution of cities: x-treme economies," MPRA Paper 13518, University Library of Munich, Germany.
  2. Philip McCann, 2005. "Transport costs and new economic geography," Journal of Economic Geography, Oxford University Press, vol. 5(3), pages 305-318, June.
  3. Kwok Tong Soo, 2004. "Zipfs Law for Cities: A Cross Country Investigation," CEP Discussion Papers dp0641, Centre for Economic Performance, LSE.
  4. Duncan Black & Vernon Henderson, 2003. "Urban evolution in the USA," Journal of Economic Geography, Oxford University Press, vol. 3(4), pages 343-372, October.
  5. Nicholas A. Christakis & James H. Fowler & Guido W. Imbens & Karthik Kalyanaraman, 2010. "An Empirical Model for Strategic Network Formation," NBER Working Papers 16039, National Bureau of Economic Research, Inc.
  6. Angelo Mele, 2010. "A structural model of segregation in social networks," CeMMAP working papers CWP32/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
  8. Kristian Behrens & Giordano Mion & Yasusada Murata & Jens Südekum, 2011. "Spatial Frictions," CEP Discussion Papers dp1108, Centre for Economic Performance, LSE.
  9. Nicholas Christakis & James Fowler & Guido Imbens & Karthik Kalyanaraman, 2010. "An empirical model for strategic network foundation," CeMMAP working papers CWP16/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  10. Jan Eeckhout, 2004. "Gibrat's Law for (All) Cities," American Economic Review, American Economic Association, vol. 94(5), pages 1429-1451, December.
  11. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
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