Abstract
The fertility decline during the demographic transition is often viewed within the frameworks of innovation and adjustment. According to the innovation perspective, this process is mostly driven by the diffusion of new knowledge and attitudes, whereas in the adaptationist perspective fertility decline is seen primarily as an adaptation to changing circumstances. In this contribution, we present models that allow us to simulate fertility declines that are solely driven by the diffusion of information structured by social and spatial variation in communication links. Using these models, we explore the question of whether observed social and spatiotemporal patterns of the fertility transition could be shaped by communication processes alone. The potential of these models is explored in a case study of Sweden. We run simulations on a full individual-level sample of the married female population aged 20–49 in 1880, which is around the time when the fertility transition started in Sweden. The population is divided into three social classes (elite, farmers, workers and others). As proxies for communication links, we use migration links. The simulation outcomes are contrasted with the observed fertility decline patterns in Sweden between 1880 and 1900. Our simulations demonstrate that communication structured by social and spatial variation in communication links could have shaped a substantial share of the observed social class and spatiotemporal characteristics of the fertility decline during the demographic transition.
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- 1.
North Atlantic Population Project.
- 2.
Integrated Public Use Microdata Series.
- 3.
While our simulation models are driven by simulated communication processes only, in choosing our vanguard adopters for the starting conditions we do, however, consider in one scenario variation in adaptation pressure (see below).
- 4.
Measured by calculating the spherical distances between the regional geographical centroids.
- 5.
We decided to focus the map on the second decade, as in this decade all three social classes experienced at least some decline. We thus consider this second decade to be more informative in terms of the spatial decline patterns.
- 6.
- 7.
The unaccounted adjustment incentives and pressures are likely to vary across space and time. They include not only variation in infant mortality and socio-economic factors that directly affect the costs of having children, but also variation in social norms that, for example, condemn the use of contraceptive techniques. The latter might create indirect costs, as in areas in which such social norms are widespread individuals who adopt a fertility control behaviour might face a loss of social capital, which Bourdieu and Wacquant (1992, p. 119) define as the resources that “accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition.” Social capital losses might also have repercussions in terms of access to income opportunities.
- 8.
These mechanisms may have been operating simultaneously, but we will keep them separate in our model specifications.
- 9.
Initially, we also considered obtaining as additional starting scenario estimates of the share of individuals who had already adopted fertility control strategies from the CWR levels by social class and region of residence in 1880. However, Fig. 13.2 shows that there is substantial variation in the levels at which specific social classes in specific areas experienced the onset of the decline. We therefore believe that it would be a very strong assumption to claim that variation in CWR levels by social class and region at a single point in time would provide information about the share of individuals who had already adopted the new behaviour.
- 10.
For the women who were in 1880 still living in their region of birth, the share of adopters in the region of birth and the region of residence are identical.
- 11.
We allow trickle-down effects from the vanguard group only in the region of residence, as we consider it rather unlikely that women would copy behaviour from the vanguard group in their region of birth if they were no longer living in that region.
- 12.
A reviewer has pointed out that we could implement backward influences in the social adaptation algorithm as well. We agree, but doing so would greatly increase the complexity of that model, as we would need to account for the share of women of a specific social class who had moved to other regions. We thus decided to stick with this simpler specification of our social adaptation algorithm, which requires just one equation.
- 13.
The time periods in the model outcomes presented in this contribution relate roughly to years, as most of the decline occurred within 50 time periods, which correspond with the 50 years between 1880 and 1930 in which Sweden experienced most of the fertility transition (Dyson 2011).
- 14.
As an alternative, we could have attempted to determine the likelihood that a woman adopted fertility control strategies based on her recent fertility history; i.e., by the number of children linked to that woman by the mother locator.
- 15.
The farmers in Stockholm city had the lowest levels throughout the period. This might be related to their small numbers, and the possibility that farmers who were living in the capital formed a very selective group.
- 16.
We also ran all of the combinations of wr and ws presented here with x = 15 and x = 20, but the outcomes suggest that an increase of x predominantly just affects the speed of the process. Thus, we focus in the presentation of the results on the outcomes obtained with x = 10.
- 17.
If, for instance, one individual out of 100 individuals in a social group adopts the new behaviour, this implies that one percent of all of the women has adopted the behaviour. By contrast, the adoption of the behaviour by one out of 10,000 women increases the adoption risks in this social group to a much lower degree. It is relevant to note that we believe that this is an inherent property of the diffusion process, and not just an unintended property of the social adaptation algorithm that we specified. Our social influence and social learning algorithms also have the same property.
- 18.
In order to generate a dataset with a total n that was similar to our standard dataset, we derived the target group n by dividing the total number of women in the simulation by the 75 groups (three social groups in 25 regions). We then sampled or duplicated observations to obtain the targeted group n in each group.
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Acknowledgements
The authors would like to thank Frans Willekens for discussions on the specifications of the models, and for his input and suggestions on earlier versions of this contribution. We also thank the anonymous reviewers and the editors for their comments and suggestions, and Miriam Hils for language editing. This work is part of the project “Towards the modern family. Socioeconomic stratification, family formation and fertility in a historical perspective”, funded by the Swedish Research Council and the Crafoord Foundation.
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Appendix 1: Map of Swedish Counties in 1880
Appendix 1: Map of Swedish Counties in 1880
Base Map: The Swedish National Archives, MPIDR Population History GIS Collection
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Klüsener, S., Scalone, F., Dribe, M. (2017). Exploring the Role of Communication in Shaping Fertility Transition Patterns in Space and Time. In: Grow, A., Van Bavel, J. (eds) Agent-Based Modelling in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-32283-4_13
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