Military conflict and the rise of urban Europe

Abstract

We present new evidence about the relationship between military conflict and city population growth in Europe from the fall of Charlemagne’s empire to the start of the Industrial Revolution. Military conflict was a main feature of European history. We argue that cities were safe harbors from conflict threats. To test this argument, we construct a novel database that geocodes the locations of more than 800 conflicts between 800 and 1799. We find a significant, positive, and robust relationship that runs from conflict exposure to city population growth. Our analysis suggests that military conflict played a key role in the rise of urban Europe.

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Fig. 1
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Notes

  1. 1.

    City founding years often long preceded the fall of Charlemagne’s empire. Boone (2013, p. 221) writes: “Broaching the subject of the origins of medieval urban Europe may wrongly suggest that the city was a totally new phenomenon in European history. In fact, in many cases medieval urbanity was built on a solid urban tradition, at least in those parts of Europe heavily influenced by the Roman Empire or Romanitas.” After the fall of the Roman Empire, urban centers typically saw decay, but did not typically disappear (Boone 2013, p. 222). In addition, new cities were founded during the medieval era throughout Europe including Central Europe (Livi-Bacci 2000, pp. 21–27).

  2. 2.

    Anecdotes highlight the historical role of cities as safe harbors beyond Europe. For example, Reid (2012, pp. 3–4) writes about Sub-Saharan Africa: “Elsewhere, warfare has been one of the drivers of urbanization, especially in the Western half of the continent; in some areas, the roots of urbanization lie in itinerant royal camps comprising armies and their enormous entourages, whereas in other circumstances, communities have come together in fortified urban clusters for protection, a phenomenon associated particularly with the nineteenth century—among the Yoruba, for example, or the Nyamwezi.” [Our italics].

  3. 3.

    Thus, we use all available population data, including for cities with less than 5000 inhabitants at some point in time. Table A2 of the online appendix shows the result for the most stringent specification (i.e., column 4 of Table 3) when we (i) exclude cities that never reached 10,000 inhabitants and (ii) only include city-century observations for city populations greater than or equal to 10,000. For (i), the estimate for \(C_{i,g,t-1}\) is 0.067, with p-value 0.031. For (ii), the estimate for \(C_{i,g,t-1}\) is 0.063, with p-value 0.057 (we lose nearly 40 % of observations). Table A9 of the online appendix shows the result for the most stringent specification when we normalize per-century city populations according to Black and Henderson (1999). The estimate for \(C_{i,g,t-1}\) is 0.092, with p-value 0.020.

  4. 4.

    The Bairoch et al. data do not include 1100. Thus, we interpolate (but never extrapolate) observations for 1100. de Vries (1984) is an alternative data source for European historical urban populations. However, the de Vries data do not start until 1500. Bosker et al. (2013) compare the Bairoch et al. and de Vries data for each century from 1500 to 1800. They find very similar estimates for urban populations; the correlation coefficients range between 0.986 and 0.992.

  5. 5.

    The nature of warfare changed dramatically over the nineteenth century due to improvements in transport and communications technologies and the rise of the mass army (Onorato et al. 2014).

  6. 6.

    We updated the urban population data according to Bosker et al. (2013) for Bruges, Cordoba, London, Palermo, and Paris.

  7. 7.

    Tilly (1992) and Jaques (2007) are two other sources for historical conflict data, both of which support the argument that military conflict was a defining feature of European history.

  8. 8.

    We use a cylindrical equal area map projection with geometric center (longitude, latitude)=(10.00735, 46.76396), near Davos, Switzerland. Figure 1 displays data in the projection of analysis.

  9. 9.

    Reyerson (1999) claims that medieval horseback travel from Paris to the Mediterranean coast, a distance of approximately 750 km, took 12 days. At a rate of 50 km per day, this trip would take 15 days. This duration is roughly in line with Reyerson’s claim about medieval horseback travel time for Italy.

  10. 10.

    Table A8 of the online appendix shows the results for the fixed effects specification (i.e., column 1 of Table 3) and the most stringent specification (i.e., column 4 of Table 3) when we use 75 km \(\times \) 75 km grid cells. For the fixed effects specification, the estimate for \(C_{i,g,t-1}\) is 0.153, with p-value 0.002. For the most stringent specification, the estimate for \(C_{i,g,t-1}\) is 0.049, with p-value 0.095.

  11. 11.

    Table 2 shows the shares of sample cities and grid cells that saw (at least one) conflict by century. On average, 27 % of cities and 19 % of grid cells saw conflict over 800–1799. The smallest share of cities saw conflict over the tenth century (9 %), while the largest share saw conflict over the eighteenth century (54 %). Similarly, the smallest share of grid cells saw conflict over the tenth century (6 %), while the largest share saw conflict over the eighteenth century (43 %).

  12. 12.

    Thus, the first observation of \(P_{i,g,t}\) is for 900, because the first observation of \(C_{i,g,t-1}\) measures conflict exposure over 800–99.

  13. 13.

    Table A15 of the online appendix shows the results when we adjust standard errors for spatial correlation according to Conley (1999) for the most stringent specification (i.e., column 4 of Table 3). To compute spatial correlation-adjusted standard errors, we follow the routine in Fetzer (2014), who extends Hsiang (2010). We assume that spatial correlation linearly decreases in the distance between each sample city up to cutoffs of 100, 200, 300, 400, or 500 km, respectively. The estimates for \(C_{i,g,t-1}\) are unchanged, with p-values that range from 0.003 to 0.004.

  14. 14.

    Initial log city populations refer to the first available observation for each sample city.

  15. 15.

    Table A3 of the online appendix shows the result for the most stringent specification (i.e., column 4 of Table 3) when we add city-specific time trends (rather than grid cell-specific time trends). The estimate for \(C_{i,g,t-1}\) is 0.044, with p-value 0.083 (standard errors clustered at city level) or p-value 0.147 (standard errors clustered at grid-cell level). Given that our dependent variable \(P_{i,g,t}\) is in logs, the grid cell-specific (or city-specific) time trends capture exponential trends (Wooldridge 2009, p. 361). Still, Table A3 of the online appendix shows the results for the most stringent specification when we add a quadratic time trend at the city level or grid-cell level. For the former, the estimate for \(C_{i,g,t-1}\) is 0.039, with p-value 0.139 (standard errors clustered at city level) or p-value 0.208 (standard errors clustered at grid-cell level). For the latter, the estimate for \(C_{i,g,t-1}\) is 0.038, with p-value 0.165.

  16. 16.

    Given network effects, Atlantic trade may have been important for cities near, but not along, the Atlantic coast. Table A7 of the online appendix shows the result that interacts Atlantic coast grid cells with century fixed effects for the most stringent specification (i.e, column 4 of Table 3). The estimate for \(C_{i,g,t-1}\) is 0.049, with p-value 0.076. This table also shows the result for Atlantic coast grid cells and neighboring grid cells. The estimate for \(C_{i,g,t-1}\) is very similar as before. To account for Hanseatic trade, we code Hanseatic cities according to the list in Dollinger (1964, pp. ix–x). Table A7 of the online appendix shows the results that interact Hanseatic cities or Hanseatic grid cells with century fixed effects. The estimates for \(C_{i,g,t-1}\) range from 0.068 to 0.069, with p-values that range from 0.027 to 0.029.

  17. 17.

    These data are available for grid cells of roughly 55 km \(\times \) 40 km. Bosker et al. match the soil quality data to cities based on latitudes and longitudes.

  18. 18.

    Andersen et al. (2015) provide data on soil suitability for a plow-positive crop (barley) for NUTS2 units, which we match to sample cities in our database. We test three levels of barley suitability: (i) high, where Andersen et al.’s suitability index exceeds 70, (ii) good, where this index exceeds 55, and (iii) medium, where this index exceeds 40. Table A14 of the online appendix shows the results when we interact soil suitability for barley with century fixed effects for the most stringent specification (i.e., column 4 of Table 3). The estimates for \(C_{i,g,t-1}\) range from 0.069 to 0.072, with p-values that range from 0.034 to 0.045.

  19. 19.

    To construct the potato suitability data, we follow the Nunn-Qian procedure. First, we match the GIS raster file with global coverage of soil suitability for potato cultivation to each 150 km \(\times \) 150 km grid cell in our database. There are five categories of potato suitability: (i) very suitable land, (ii) suitable land, (iii) moderately suitable land, (iv) marginally suitable land, and (v) unsuitable land. Nunn and Qian define land to be suitable for potato cultivation if it falls within categories (i) to (iii). Finally, we define potato suitability as \(Potato_{i,g}\equiv \ln (1+PotatoArea_{i,g})\).

  20. 20.

    The data for the other geographic variables are from Bosker et al. (2013).

  21. 21.

    Note that these variables are “bad” controls (Angrist and Pischke 2009) in the sense that they themselves may be outcomes of military conflict.

  22. 22.

    To test whether larger cities were more attractive targets over the short term, Table A12 of the online appendix shows the results of the target effect test when we regress the conflict exposure measure \(C_{i,g,t-1}\) on lagged city populations \(P_{i,g,t-1}\), where \(C_{i,g,t-1}\) now equals 1 if there was a military conflict in grid cell g over the first z years of century t, where \(z=10, 20, 30, 40, 50\). The coefficients for \(P_{i,g,t-1}\) are never significant.

  23. 23.

    In the online appendix, we account for the target effect in another way, by including the lagged dependent variable and re-running the specifications from Table 3. Including the lagged dependent variable induces Nickell bias (Nickell 1981), particularly if the panel’s time dimension T is small (for our sample, \(T=8\)). Still, Table A6 shows the main results when we include lagged log city population as a control. The estimates for \(C_{i,g,t-1}\) range from 0.048 to 0.080, with p-values that range from 0.005 to 0.140 (we lose 20 % of observations). For comparison, the original estimates range from 0.063 to 0.113, with p-values that range from 0.004 to 0.055. Thus, including lagged log city population does not change the main results by much. To address Nickell bias, we can use GMM estimation (Arellano and Bond 1991). However, GMM requires strong assumptions (Angrist and Pischke 2009, p. 245). Namely, GMM uses past differenced lags to instrument for the lagged dependent variable; it is unlikely that past differenced lags are not correlated with the differenced residuals. Still, Table A13 shows the main results when we use GMM. The estimates for \(C_{i,g,t-1}\) range from 0.024 to 0.062, with p-values that range from 0.013 to 0.321 (we lose more than 35 % of observations).

  24. 24.

    Figure A1 of the online appendix shows the results for the most stringent specification (i.e., column 4 of Table 3) when we exclude each country one by one (there are 26 countries). The estimates for \(C_{i,g,t-1}\) range from 0.044 to 0.077, with p-values that range from 0.014 to 0.098 (24 of 26 p-values are less than 0.050). The lowest point estimate (0.044) occurs when we exclude Spain. Still, this estimate remains significant at the 10 % level. Over the medieval period, Christian rulers expelled Muslim invaders from Spain (the Reconquista). To defend newly recaptured territory, such rulers established fortified cities. To promote repopulation, they often granted individual freedoms to new urban inhabitants (O’Callaghan 2002, pp. 699–700).

  25. 25.

    Furthermore, excluding the capital city and largest city today for each sample country does not change the main results.

  26. 26.

    The estimate for conflict exposure is positive but no longer significant when we restrict the conflict sample to the pre-early modern era (e.g., column 4 of Table A5 of the online appendix). Tilly (1992), Parker (1996), and Gennaioli and Voth (2014) highlight the importance of new urban fortifications in early modern Europe called the trace italienne. New fortifications may have reduced the probability of wartime urban destruction. For example, Bosker et al. (2013) provide data for the number of times that a sample city was plundered each century. Between the start of the ninth century and the end of the twelfth century, sample cities were plundered 105 times. However, between the start of the fifteenth century and the end of the eighteenth century, sample cities were only plundered 65 times. Furthermore, the most plundering (61 times) took place over the ninth century, while the least plundering (5 times) took place over the eighteenth century. This evidence suggests that post-fifteenth century improvements in defensive structures may have reduced the likelihood of wartime urban destruction, which may help explain why our results suggest that the safe harbor effect was more important during the early modern era than before. A related explanation is that the safe harbor effect may have become more important after the Black Death and the emergence of a post-Malthusian economy. Greater per capita income translated into greater tax revenues, which (i) increased the likelihood of military conflict (Voigtländer and Voth 2013a, b) and (ii) may have increased the ability of polities to construct new fortifications, thereby reducing the likelihood of wartime urban destruction. This explanation is consistent with the result in column 5 of Table 5, which shows that a target effect was less likely if a city had the military strength to protect its wealth.

  27. 27.

    Table A10 of the online appendix shows the result for the most stringent specification (i.e., column 4 of Table 3) when we use the number of conflicts and the squared number of conflicts as our variables of interest. The estimate for \(C_{i,g,t-1}\) is 0.023, with p-value 0.051. The estimate for the quadratic term is \(-0.001\), with p-value 0.118. The coefficient for the quadratic term is negative. However, (i) the magnitude is very small and (ii) the negative effect does not apply until 12 conflicts are reached. This number is large; nearly all sample cities were exposed to less conflict over 800–1799.

  28. 28.

    As described in Sect. 2, a city without fortified walls could still have outer rings of conjoined dwellings that functioned as barriers, or could have defensive palisades or ramparts, or be situated in a naturally protected location, thereby enabling small groups of defenders to fend off larger groups of attackers.

  29. 29.

    Tracy’s definition of “walled city” counts single or double stone walls, gun platforms placed outside of walls, and bastioned traces, but excludes defensive palisades and ramparts.

  30. 30.

    The mappings from historical provinces to modern German states are approximations. Tracy presents data for eleven historical provinces, of which we exclude two (East Prussia and Silesia) that do not fall within the borders of modern Germany. Of the nine historical provinces that we include, we map Anhalt to the modern state of Saxony-Anhalt, Brandenburg to Brandenburg, Hesse to Hesse, Meckenburg and Pomerania to Mecklenburg-Vorpommern, Rhineland to Rhineland-Palatinate, Saxony (or, Old Saxony) to Lower Saxony, Schleswig-Holstein to Schleswig-Holstein, and Thuringia to Thuringia.

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Acknowledgments

We thank editor Oded Galor and three anonymous referees for valuable comments. Similarly, we thank Pablo Beramendi, Timothy Besley, Carles Boix, Roberto Bonfatti, Eltjo Buringh, Eric Chaney, James Fearon, Jeffry Frieden, Edward Glaeser, Philip Hoffman, Horacio Larreguy, James Morrow, Tommaso Nannicini, Nathan Nunn, Margaret Peters, Hugh Rockoff, Jean-Laurent Rosenthal, Kenneth Shepsle, Ugo Troiano, Julian Wucherpfennig, Jan Luiten van Zanden, Daniel Ziblatt, Fabrizio Zilibotti, and seminar participants at Birmingham, Bristol, Harvard, Harvard PIEP, LSE, Michigan, Modena, NES, Nottingham, PSE, UCL, and numerous conferences. We thank Maarten Bosker, Eltjo Buringh, and Jan Luiten van Zanden for generous data-sharing, and Nicola Fontana, Giovanni Marin, Michael Rochlitz, Nicole Scholtz, and Kerby Shedden for excellent data help. Finally, we thank the National Science Foundation for financial support through Grant SES-1227237.

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Dincecco, M., Onorato, M.G. Military conflict and the rise of urban Europe. J Econ Growth 21, 259–282 (2016). https://doi.org/10.1007/s10887-016-9129-4

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Keywords

  • Warfare
  • Cities
  • Political and economic development
  • Europe

JEL Classification

  • C20
  • O10
  • N40
  • N90
  • P48
  • R11