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Towns (and villages): definitions and implications in a historical setting

Abstract

The measurement of urbanization rates and other uses of statistical information, for example the use of historical town growth to measure long-term economic growth, are usually based on an ad hoc population threshold to define and practically classify settlements as towns. The method, however, trades off accuracy and precision for convenience and simplicity. This paper proposes a new threshold that uses the town size distribution together with agricultural data to derive an appropriate cutoff value. The relevance of agricultural income is integrated into the classification scheme through the differential effect of local agricultural endowments on settlement size. The threshold is chosen such that the size of towns above the cutoff is statistically not influenced by local agricultural endowments, while the size of villages, which is below the threshold, is indeed shaped by them. This new approach is practically demonstrated with an application to the urban system of the nineteenth century in the German region of Saxony. This setting is used to investigate the relevance of a different classification for the development of urbanization over time and Gibrat’s law. The results demonstrate that the underlying classification scheme matters strongly for the conclusions drawn from historical urban data. They also indicate that the use of a common population threshold for a comparative analysis or temporal comparisons in a historical context increases the misclassifications of settlements.

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Notes

  1. Although the terms town and city have different connotations in certain contexts, I do not make this distinction here and will use the terms interchangeably.

  2. These data sets, especially Bairoch’s, contain some information about the population of included towns when these were below the 5000 threshold, which is used in some studies. Besides selection issues, there is also the problem of precision since Bairoch rounds the numbers to full thousands.

  3. Historically this seems to be similar in other fields, for example sociology (Martindale 1958).

  4. There exist more than that, for example Tilly (1976) develops another one. It focuses on concepts of market structure and relationships.

  5. See Cantoni and Yuchtman (2014) for some historical context on the development of these rights in Germany and Ploeckl (2012) for an application using such a concept.

  6. Such a classification has been used focusing on one country only, for example Ploeckl (2010a).

  7. They do attempt to provide the size of towns for the time before they reach the thresholds, which gives from some authors using these data the appearance of a lower threshold.

  8. The production of non-agricultural goods can include food processing just not the initial production of food inputs.

  9. The specific relevance of the two criteria that are not used in the classification process is discussed in online appendix Sect. 2.

  10. The set is based on data used in Ploeckl (2010a).

  11. See Ploeckl (2015) for a discussion of this entry.

  12. The revenue distribution scheme of the Zollverein was based on a state’s population; the states therefore agreed to consistent methods for population counts (Hahn 1984; Henderson 1984; Ploeckl 2010b).

  13. These are either official coordinates from the Saxon Landesvermessungsamt, from a historical place register or selected by the author by inspecting various maps (Blaschke and Baudisch 2006).

  14. Ruggedness is measured as the standard deviation of elevation within a 2-km radius.

  15. Data sources are described in online appendix Sect. 1.

  16. Studies have highlighted the special role of the largest town, often the capital (Ades and Glaeser 1995). Since this concerns only one specific town, the impact is limited and therefore negligible in this context.

  17. Table 1 establishes that endowments have an effect on the size of all villages.

  18. As a robustness test I also tested for values up to 10,000, the results did not change.

  19. Conducting the analysis without the additional geographic variables results in a somewhat lower threshold below 2000 inhabitants; the details are shown in online appendix as specification III.3.

  20. Clustering the observations according to modern parish boundaries leads to a range of threshold values between 3360 and 4580 for the different specifications, see figures A4 to A7 in online appendix. Due to the very large number of observations that are a single location in a modern parish, the regular estimation is the preferred one.

  21. Waechter (1901, p194) suggests that a number of smaller towns had specialized in some non-agricultural sectors but did lose that specialization over the course of the nineteenth century. This observation fits well with the proposed explanation.

  22. A number of studies, for example Rosen and Resnick (1980), do find an impact of a different size threshold on these statistical regularities, which implies that this section predominantly confirms results known to the literature.

  23. Applying a 10 % significance level the hypothesis of proportional growth can be rejected for a town size threshold below 3430.

  24. See online appendix for an alternative specification that has somewhat lower requirements but has fairly similar results.

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Acknowledgments

I want to thank Bob Allen, Rui Esteves and James Fenske for helpful discussions as well as seminar audiences at Oxford and conference audiences at the Sound Economic History Meeting, as well as the APHES, EEA and EHES meetings.

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Correspondence to Florian Ploeckl.

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Ploeckl, F. Towns (and villages): definitions and implications in a historical setting. Cliometrica 11, 269–287 (2017). https://doi.org/10.1007/s11698-016-0145-6

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Keywords

  • Towns
  • Villages
  • Geography
  • Definition
  • Classification
  • Town size

JEL Classification

  • N93
  • B49
  • O13
  • R12