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Growth in a Cross-section of Cities: Location, Increasing Returns or Random Growth?

Author

Listed:
  • Rafael Gonz�lez-Val
  • Jose Olmo

Abstract

This article analyses empirically the main existing theories on income and population city growth: increasing returns to scale, locational fundamentals and random growth. To do this we consider a large database of urban, climatological and macroeconomic data from 1,173 US cities observed in 1990 and 2000. The econometric model is robust to the presence of spatial effects. Our analysis shows the existence of increasing returns and two distinct equilibria in per-capita income and population growth. We also find important differences in the structure of productive activity, unemployment rates and geographical location between cities in low-income and high-income regimes.

Suggested Citation

  • Rafael Gonz�lez-Val & Jose Olmo, 2015. "Growth in a Cross-section of Cities: Location, Increasing Returns or Random Growth?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 230-261, June.
    • Handle: RePEc:taf:specan:v:10:y:2015:i:2:p:230-261
    DOI: 10.1080/17421772.2015.1023337
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    Cited by:

    1. Esther-Vayá & José-Ramón-García & Joaquim-Murillo & Javier-Romaní & Jordi-Suriñach, 2016. "“Economic Impact of Cruise Activity: The Port of Barcelona"," IREA Working Papers 201613, University of Barcelona, Research Institute of Applied Economics, revised Nov 2016.
    2. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    3. Pavel Cudlin & Alessandro Muolo & Kostas Rontos & Luca Salvati, 2025. "Von Thunen Revisited? An Econometric Analysis of Metropolitan Expansion and Land-Use Change in Greece," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 491-516, June.
    4. Anna Castañer & Mª Mercè Claramunt & Alba Tadeo & Javier Varea, 2016. "Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II," Working Papers XREAP2016-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Sep 2016.
    5. Catalina Bolancé & Zuhair Bahraoui & Ramon Alemany, 2015. "Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches," Working Papers XREAP2015-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Jan 2015.
    6. Anna Castañer & Mª Mercè Claramunt, 2014. "Optimal stop-loss reinsurance: a dependence analysis," Working Papers XREAP2014-04, Xarxa de Referència en Economia Aplicada (XREAP), revised Apr 2014.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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