Market potential and city growth: Spain 1860–1960


In this paper, we employ parametric and nonparametric techniques to analyse the effect of market potential on the structure and growth of Spanish cities during the period 1860–1960. Even though a few attempts have been made to analyse whether market potential might influence urban structures, this period is especially interesting because it is characterised by advances in the economic integration of the national market together with an intense process of industrialisation. By using an elaborated measure of market potential at the city level, our results show a positive influence of this market potential on city growth, although this influence is heterogeneous over time. Only changes in the market potential from 1900 have a significant effect on population growth.

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  1. 1.

    See Redding (2010) for a survey on this literature.

  2. 2.

    We use a measure of real transport costs. Combes and Lafourcade (2005) conclude that this is a better approach.

  3. 3.

    See, for example, Ringrose (1996) for further details.

  4. 4.

    A more detailed description of these processes can be found in Rosés et al. (2010).

  5. 5.

    Most of the empirical contributions to the Spanish case discussed below adopt the standard empirical methodologies developed for the analysis of NEG models. Redding (2010) offers a recent survey of these empirical methods.

  6. 6.

    Ayuda et al. (2010).

  7. 7.

    Kancs (2005) also makes use of this approach to analyse the determinants of migratory flows in the European Union.

  8. 8.

    In a similar vein, Goerlich and Mas (2009) also study the long-term determinants of the agglomeration of the population in Spain.

  9. 9.

    We also estimate the Pareto exponent using simple OLS regressions and the Hill estimator, and the results are quite similar.

  10. 10.

    However, there is a statistical relationship between Zipf’s law and the main concentration indices: Gini, Bonferroni, Amato and the Hirschman–Herfindahl index (Naldi 2003).

  11. 11.

    Provinces are Spanish NUTS3 regions.

  12. 12.

    See Martínez-Galarraga (2010) for a detailed description.

  13. 13. and

  14. 14.

    In 1930, however, road transport was not yet playing an important role, and therefore, it was not considered (Herranz 2006).

  15. 15.

    Two exceptions include Cuba, a market that received a high percentage of Spanish exports, above all in the mid-nineteenth century (18.5 % of the total but only 5.3 % in 1913 and 2.1 % in 1930), and Argentina, whose market exceeded the 5 % threshold on the eve of the First World War. They are excluded due to data restrictions regarding GDP at current prices. However, it ought to be the case that the limited size of their markets and, especially, the great distance separating them from the Peninsula would minimise the cost of their exclusion.

  16. 16.

    Overall, these four countries accounted for 62.4 % of Spanish exports in 1865/1869, 57.8 % in 1895/1899, 58.0 % in 1910/1913 and 58.9 % in 1931/1935, with France and Great Britain the main markets.

  17. 17.

    Crafts (2005) gives disaggregated information for regional GDP in Great Britain. Hence, it is possible to calculate market potential, not by assigning all the economic activity in Britain to London but rather by distributing it between the nodes selected for each of the 12 regions. However, this approach provides similar results.

  18. 18.

    Therefore, the self-potential of each city is obtained from provincial market potential in proportion to the population of the city over the overall provincial population. For example, the population of Madrid City in 1860 was 57 % of the total population of the Madrid Province, and this is the same proportion that we apply to province domestic market potential to obtain the own market potential of Madrid City. This value can be considered to be a lower bound because the share of province market potential corresponding to the largest cities is probably greater than their proportion of the total population, as nonlinear behaviours and increasing returns to scale can be involved in a NEG framework.

  19. 19.

    The local polynomial smoother fits the growth rate g it  = ( ln S it+1 – ln S it ) to a polynomial form of ln MP it−1 via locally weighted least squares. We use the lpolyci command in STATA with the following options: local mean smoothing, a Gaussian kernel function to calculate the locally weighted polynomial regression and a bandwidth determined using Silverman’s (1986) rule of thumb.

  20. 20.

    We must consider homogeneous periods because, if we mixed different time periods (1860–1900 and decennial data since 1900) the later periods could overpower the estimated effects just because we would have more decade observations.

  21. 21.

    Note that as new cities enter the sample over time and Eq. (7) includes past population growth rates, the total number of observations in our regressions does not coincide with the sum of the number of cities by period shown in Table 1.

  22. 22.

    Nevertheless, we also estimate using provincial market potential instead of city market potential, and the results are similar. These results are shown in ‘Appendix’, Tables 7 (OLS results) and 8 (IV results).

  23. 23.

    This positive effect is expected to fall with the market potential size for many cities, given the transition to a U-shaped pattern previously observed in Fig. 3. To check this possible nonlinear relationship, we rerun Eq. (7) using the squared market potential variable, γ(ln MP it−1)2, instead of β ln MP it−1, expecting a γ positive coefficient. The results reveal a slight nonlinear relationship (estimated coefficients are between 0.003 and 0.004) only significant in the 1930–1960 period (as Fig. 3 shows) and for the pool 1860–1960. These results are available from the authors on request.

  24. 24.

    We use regional population density instead of city population density because provincial boundaries remain constant over time, while city boundaries change in some cases.

  25. 25.

    Thus, population density values from 1787 are used to estimate market potential in 1860, 1860 values to estimate market potential in 1900 and the values from 1900 to estimate market potential in 1930.

  26. 26.

    The complete results of the reduced regressions, first-stage regressions and all the tests, not shown for size restrictions, are available from the authors on request.


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This research received financial support from projects ECO2012-39169-C03-02, ECO2013-45969-P and ECO2013-41310-R (Ministerio de Economía y Competitividad), 2014SGR00420 (Generalitat de Catalunya), DGA (ADETRE research group) and FEDER. We acknowledge the helpful comments from Jeffrey Lin and Gabriel Ahlfeldt. We are also grateful to the participants at the 7th Meeting of the Urban Economics Association, at the 59th Annual North American Meetings of the Regional Science Association International (Ottawa, 2012), at the XXXVII Symposium of Economic Analysis (Vigo 2012), at the Sixth Iberian Economic History Conference (Zaragoza 2013), at the Seventh World Congress of Cliometrics (Honolulu 2013) and at the Royal Economic Society Conference (Manchester 2014). We thank F. J. Goerlich and Julio Martinez-Galarraga for the provision of some unpublished data.

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Correspondence to Elisabet Viladecans-Marsal.



See Tables 7 and 8.

Table 7 Spanish city size growth: provincial market potential, OLS estimates
Table 8 Spanish city size growth: provincial market potential, IV estimates

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González-Val, R., Tirado-Fabregat, D.A. & Viladecans-Marsal, E. Market potential and city growth: Spain 1860–1960. Cliometrica 11, 31–61 (2017).

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  • Market potential
  • Urban structure
  • City growth
  • Economic history

JEL Classification

  • R0
  • N9
  • O18
  • N64
  • F14