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The rural exodus and the rise of Europe

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Abstract

We build a unified model of growth and internal migration and identify its deep parameters using an original set of Swedish data. Our structural estimation and counterfactual experiments suggest that conditions of migration between the countryside and cities have strongly shaped the timing and the intensity of the transition to growth. Mobility cost had to be low enough to enable population movement. Furthermore, initial productivity in rural industries had to be moderate to sustain the first phase of industrialization appearing in the countryside without delaying too much the second phase of industrialization taking place in cities. More than the initial productivity of rural industries or migration costs alone, what truly mattered for the transition to modern economic growth was the interplay between these two elements. By contrast, we evidence a poor role for mortality decline in the whole process. Finally, we discuss why our conclusions on Sweden are exemplary for the rest of Western Europe.

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Notes

  1. In 1850, approximately 10% of the Swedish population was living in cities; however, by 1960, this share had increased to 52.3%.

  2. If both agriculture and rural industries use rural abilities, they do so under different technological regimes. Skills are not initially equally distributed among adults, such that our framework is a heterogeneous agents model.

  3. Interestingly enough, Edvinsson (2017) evidences the existence of a Malthusian regime in Sweden before 1800 while positive checks may have persisted even until 1920.

  4. Considering income differentials between cities and the countryside as a driving force for urbanization is in line with most of the literature on the topic. See, for instance, Gollin et al. (2016).

  5. In Sect. 4.5, we discuss the reasons why we do not try to match additional moments. The main reason pertains to issues with the definitions of GDP per capita and school enrollment rates.

  6. The development of rural industries during the eve of the Industrial Revolution has often been qualified as protoindustrialization after Mendels (1972). Economic historians have had severe disagreements on the reality of this phenomenon, but they have all agreed on the fact that prior to the so-called Industrial Revolution, a non-negligible portion of industrial work was located in rural areas. See Ogilvie and Cerman (1996) for a complete discussion of the concept of protoindustrialization, as well as Sect. 6.

  7. Some papers have documented a significant impact of other types of mortality, such as young adult mortality and longevity, on the economic take-off. See, among others, the enlightening papers of Tamura (2002), Cervellati and Sunde (2011), Strulik and Werner (2016), Boucekkine et al. (2002), Strulik and Weisdorf (2014) and Voigtländer and Voth (2012).

  8. A large number of other refinements to unified growth theory have been produced. Galor (2011) provides an enlightening review of this literature.

  9. Other papers have applied calibration techniques to UGT. Lagerlöf (2006) presents a numerical exercise conducted with the model of Galor and Weil (2000) by choosing parameters either from the literature or currently observed moments. Cervellati and Sunde (2005) and Strulik and Weisdorf (2014) present illustrative numerical simulations that highlight the respective roles of longevity and child survival. Cervellati et al. (2013) use panel data for 59 countries beginning in 1880 to structurally estimate a unified growth model.

  10. Data comparability issues necessarily arise. In “Online Appendix A.1”, we describe the general procedures we used for data harmonization and smoothing. All statistics are computed using current Swedish borders.

  11. Centralbyrån (1969) provides an overview of Swedish cities and their year of foundation.

  12. This exercise has been possible thanks to the help of Umeå University and its retrieval tool, discussed in “Online Appendix A.2”.

  13. Schön (2010) also documents a share of 10% of the total population living in towns and cities for the first half of the nineteenth century.

  14. Brändström (1993) presents Swedish infant mortality rates at the provincial level and evidence on intra-rural and intra-urban differentials.

  15. We discuss the method and the advantages of using alternative fertility measures in “Online Appendix A.3”.

  16. See “Online Appendix A.4” for details.

  17. In his contribution to the EH.net Encyclopedia, Schön (2008) argues that “During the first half of the nineteenth century (...) many non-agrarian activities such as the iron industry, the saw mill industry and many crafts as well as domestic, religious and military services were performed in rural areas.” This argument is further developed in key contributions such as Schön (2010, 2012).

  18. Even if our theory does not offer any predictions regarding wages per sector, the latter may be used to evaluate whether our presentation of the timing of growth is plausible. In Figure 20 of “Online Appendix A.5”, we present wages by sector, normalized to 100 in 1800, and we can again identify the fact that industry started to outperform agriculture around 1820, at least in terms of growth rates.

  19. It has been widely documented that although school became compulsory in 1842 in Sweden, enrollment rates stayed well below 100% during the nineteenth century; see Flora (1983), Benavot and Riddle (1988) or Soysal and Strang (1989), among many others.

  20. Enrollment rates in integrated secondary and tertiary education indicate the number of pupils in the age groups 15–19 and 20–24, respectively. For details, see de la Croix et al. (2008).

  21. In setting up our model in this way, we disregard potentially important phenomena such as savings or intergenerational co-residence. On this latter point, see the recent contribution of Pensieroso and Sommacal (2019).

  22. This simplifying assumption implies that all members of the same dynasty share the same rural abilities.

  23. This assumption is in line with the history of Sweden but even more generally of Western European countries. See, among others, the key contributions of Lampe and Sharp (2015), Bairoch (1997), Berger et al. (2012) and Schön (2010).

  24. Such a hypothesis is supported by Squicciarini and Voigtländer (2015) and Lees and Lees (2007), among many others.

  25. See, for instance, the enlightening contributions of Simon and Nardinelli (1996, 2002) and Brunt and García-Peñalosa (2021) for the cases of England and the US. The magnifying effects of agglomeration (through peer effects and imitation) on the accumulation of human capital in cities compared to that in rural areas is theorized, for instance, by Glaeser (1999). The specific use of human capital in industry is also a central assumption in papers such as Tamura (2002).

  26. In line with the literature from Becker (1960) to Galor (2011), fertility decisions are represented with continuous numbers.

  27. Importantly enough, we do not assume differences in preferences between urban and rural persons because migration decisions are endogenous; it would allow people to decide on their own preferences. Nevertheless, assuming, for instance, that urban persons value more the human capital of their children than rural persons may help to capture the dynamics of the trade-off between quality and quantity over time in a better way. We present this alternative framework in “Online Appendix D.2”.

  28. For a discussion of childlessness, see Baudin et al. (2015).

  29. The perfect mobility between rural sectors ensures that \(g_{y_t^{{\mathcal {I}}}}=g_{y_t^{{\mathcal {A}}}}\).

  30. The condition \(\zeta ^U>\zeta ^R\) is satisfied in our calibration-simulation exercise, see Sect. 4.4.

  31. Population size could be stabilized at a level below \({\bar{N}}\) along the BGP relying on additional mechanisms like international immigration or public policies increasing net fertility to one.

  32. See “Online Appendix B.3” for a proof.

  33. We illustrate this argument in “Online Appendix D.1”, where we propose an alternative calibration and simulation exercise with \(a^i={\bar{a}}\) for all dynasties. Even if satisfactory, the fit of the predicted moments reduces significantly compared to our benchmark framework. In particular, the educational transition is less smooth. All rural parents start to educate their children at the exact same moment; the educational revolution is “rugged”.

  34. We describe the general procedure in more detail in “Online Appendix A.1”.

  35. Hutterites are German Anabaptists reputed to have fertility rates close to maximal reproduction capacities.

  36. See Baudin and Stelter (2021) for more details.

  37. In “Online Appendix C.4”, we show that, in our simulations, migrations were quantitatively much less important before 1800 than after, and even almost absent for some years. This result has to be interpreted carefully: in a context where migration costs represent almost 30% of the average potential income and given that families reallocated themselves in 1760 already, migration was not a viable option during some periods for the vast majority of the population. In reality, sizable internal migration existed before the industrialization in Sweden, especially short distance migration as explained by Dribe (2003) as well as Andersson (2018). One could imagine an extension of our model where migration costs would be heterogeneous. It would allow for short distance migration even in pre-industrial times. Though more realistic, such an extension would severely complexify our theory without changing its main predictions and its overall quantitative fit.

  38. It corresponds to approximately 11.7% of the average available income. This value is plausible, as Gary (2018) estimates that on average, in 1760, fewer than 50 days of casual work were required to earn a subsistence basket.

  39. Notice that in alternative parameter sets, poverty may lead to the disappearance of some dynasties.

  40. Our model predicts an increase in urban fertility between the first two periods, a result that is not in line with the data. This is because the dynasties are not perfectly allocated across areas in the initial state and then they reallocate themselves. We find that in 1760, the initial period in the simulation, some dynasties relocated from the countryside to a city (see Figure 31a and 31b in “Online Appendix C.4”). Approximately 5% of young adults in rural areas left their place of origin and moved to a city, where they represented 30% of the young adults. These relatively poor dynasties were associated with a significant reduction in average fertility in cities. They had low levels of human capital, and paying \(\kappa\) caused them to live in a Malthusian regime.

  41. These values correspond to the \(\hbox {R}^{2}\) of an ordinary least squares regression in which the observed TFR is the dependent variable and the predicted TFR is the independent variable. Another way to evaluate the quality of the model’s fit is to compute the average distance between the observed and predicted moments in percentage points. In the case of fertility, the distance equals 10.5% in cities and 6.1% in the countryside. See “Online Appendix C.1” for a further discussion.

  42. Even if the fit of the predicted moments speaks in favor of our estimation strategy, it does not guarantee that our set of estimated parameters is located in a well-behaved region of the parameter space nor that each parameter is well identified. We show in “Online Appendix C.3” that our estimation suffers from neither of these two issues.

  43. Using the GDP per capita or taking into account all sectors instead of being limited to industry and agriculture also leads to a convincing reproduction of the empirical moments; see “Online Appendix C.1”.

  44. More precisely, using Centralbyrån (1969) and Table 3, we compute the ratio between the number of urban workers and the number of urban workers plus rural industrial workers.

  45. For simplicity, we propose a constant threshold to “mimic” educational levels. In line with Galor and Weil (2000), we could also argue that the acceleration in technological progress required increasingly higher levels of education to maintain the relative educational level. Such a dynamic threshold would reduce the temporary underestimation in secondary and tertiary education enrollment rates.

  46. See details in “Online Appendix C.2”.

  47. Fertility rates below the replacement level are observed among many industrialized economies, like Japan and Germany having respectively 1.36 and 1.54 births per woman in 2019 according to the World Bank. In particular, several European countries experience what demographers have designated as lowest-low fertility levels since the 1990s (Kohler et al., 2002; Billari and Kohler, 2004; Sobotka, 2004).

  48. In Figure 21 of “Online Appendix B.2”, these persons live in the neighborhood of \({\bar{\omega }}^{j}\).

  49. Note that in the initial period, a higher \(A_0^{\mathcal {I}}\) provokes an urban exodus; this effect is quite sizable when \(A_0^{\mathcal {I}}\) is multiplied by 10.

  50. In Table 9 in “Online Appendix C.6”, we provide the series of net changes in the number of births in each area.

  51. Prussia introduced compulsory education with the famous school edict in 1717, while its industrialization was rather late (Van Horn Melton, 2003). In contrast, in England (and Wales), as the leader in industrialization, primary education was not compulsory before 1870 when compulsory primary education was established with the Elementary Education Act (Flora, 1983).

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Authors and Affiliations

  1. IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, 59000, Lille, France

    Thomas Baudin

  2. IRES, Université catholique de Louvain, Louvain-la-Neuve, Belgium

    Thomas Baudin

  3. Faculty of Business and Economics, University of Basel, 4002, Basel, Switzerland

    Robert Stelter

  4. Laboratory of Fertility and Well-Being, Max Planck Institute for Demographic Research, 18057, Rostock, Germany

    Robert Stelter

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  1. Thomas Baudin
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  2. Robert Stelter
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Correspondence to Thomas Baudin.

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We gratefully thank Oded Galor and four anonymous referees who have greatly contributed to improve this paper. We also benefited from discussions with Philippe Bocquier, Shankha Chakraborty, David de la Croix, Alexia Fürnkranz-Prskawetz, Paula Gobbi, Victor Hiller, Joël Machado, Andreas Irmen, Fabian Kindermann, Carl Mosk, Jim Oeppen, Holger Strulik, Uwe Sunde and Nico Voigtlaender. We also thank the participants of seminars and conferences in Louvain-la-Neuve, Paris, Hamburg, Aix-en-Provence, Moscow, Rostock, Nanterre and Brussels for their helpful comments. We would like to thank Annika Westberg for helping us with the SHiPs data. We are deeply grateful for the computational infrastructure provided by the Max Planck Computing and Data Facility and the assistance offered by Markus Rampp. This project has benefited from the scientific and financial support of ARC project 15/19-063 on “Family transformations” (French-speaking community of Belgium) and Project MALYNES (ANR-18-CE26-0002). Robert Stelter acknowledges financial support from the Max Geldner foundation. The traditional disclaimer applies.

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Baudin, T., Stelter, R. The rural exodus and the rise of Europe. J Econ Growth 27, 365–414 (2022). https://doi.org/10.1007/s10887-022-09206-4

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