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Rich and Poor Cities in Europe. An Urban Scaling Approach to Mapping the European Economic Transition

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  • Emanuele Strano
  • Vishal Sood

Abstract

Recent advances in the urban science make broad use of the notion of scaling. We focus here on the important scaling relationship between the gross metropolitan product (GMP) of a city and its population (pop). It has been demonstrated that GMP ∝ Y Ypopβ with β always greater than 1 and close to 1.2. This fundamental finding highlights a universal rule that holds across countries and cultures and might explain the very nature of cities. However, in an increasingly connected world, the hypothesis that the economy of a city solely depends on its population might be questionable. Using data for 248 cities in the European Union between 2005 and 2010, we found a double GMP/pop scaling regime. For West EU cities, β = 1 over the whole the period, while for post-communist cities β > 1 and increases from ∼1.2 to ∼1.4. The evolution of the scaling exponent describes the convergence of post-communist European cities to open and liberal economies. We propose a simple model of economic convergence in which, under stable political conditions, a linear GMP/pop scaling is expected for all cities. The results suggest that the GMP/pop super-linear scaling represents a phase of economic growth rather than a steady, universal urban feature. The results also suggest that relationships between cities are embedded in their political and economic context and cannot be neglected in explanations of cities, urbanization and urban economics.

Suggested Citation

  • Emanuele Strano & Vishal Sood, 2016. "Rich and Poor Cities in Europe. An Urban Scaling Approach to Mapping the European Economic Transition," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-8, August.
  • Handle: RePEc:plo:pone00:0159465
    DOI: 10.1371/journal.pone.0159465
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    References listed on IDEAS

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    2. Haohui 'Caron' Chen & Xun Li & Morgan Frank & Xiaozhen Qin & Weipan Xu & Manuel Cebrian & Iyad Rahwan, 2019. "Automation Impacts on China's Polarized Job Market," Papers 1908.05518, arXiv.org.
    3. Haohui Caron Chen & Xun Li & Morgan Frank & Xiaozhen Qin & Weipan Xu & Manuel Cebrian & Iyad Rahwan, 2022. "Automation impacts on China’s polarized job market," Journal of Computational Social Science, Springer, vol. 5(1), pages 517-535, May.
    4. Xu, Gang & Xu, Zhibang & Gu, Yanyan & Lei, Weiqian & Pan, Yupiao & Liu, Jie & Jiao, Limin, 2020. "Scaling laws in intra-urban systems and over time at the district level in Shanghai, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    5. Fabiano L Ribeiro & Joao Meirelles & Vinicius M Netto & Camilo Rodrigues Neto & Andrea Baronchelli, 2020. "On the relation between transversal and longitudinal scaling in cities," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-20, May.
    6. Joao Meirelles & Fabiano L. Ribeiro & Gabriel Cury & Claudia R. Binder & Vinicius M. Netto, 2021. "More from Less? Environmental Rebound Effects of City Size," Sustainability, MDPI, vol. 13(7), pages 1-20, April.
    7. Chenchen Shi & Naliang Guo & Xiaoping Zhu & Feng Wu, 2022. "Assessing Urban Resilience from the Perspective of Scaling Law: Evidence from Chinese Cities," Land, MDPI, vol. 11(10), pages 1-23, October.
    8. Krzysztof Cebrat & Maciej Sobczyński, 2016. "Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    9. Amin Khiali-Miab & Maarten J van Strien & Kay W Axhausen & Adrienne Grêt-Regamey, 2019. "Combining urban scaling and polycentricity to explain socio-economic status of urban regions," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
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