Our era has famously been described as the “age of migration” (Castles & Miller 2009), and indeed: between 1960 and 2010, the global number of transnational migrants more than doubled, rising from approximately 91 to 204 million (Deutschmann 2016). But what explains this increase? And why exactly is it that people move from one country to another? While a generally accepted theoretical framework for studying migration does not exist (Massey et al. 1998), migration flows have traditionally been modelled as resulting from a combination of push and pull factors (Lee 1966). Push factors may include unemployment, poverty, (civil) wars, political suppression, discrimination, and human rights violations, as well as natural disasters from droughts to earthquakes in sender countries. Pull factors range from (expected) security, employment opportunities, and prosperity, to education opportunities, tolerance, and peace in receiving countries. As this list reveals, conflicts (as focused on in the first summer school [see above]) play a major role in the first group of determinants.
Particularly large migratory flows are often triggered by civil wars, with Syria being the most prominent current example (EUI Migration Policy Centre 2016). Individuals whose lives are threatened turn into refugees and massive relocations of people happen in short time periods, neglecting formal and controlled ways of migration. When people are more or less safe, the journey does not necessarily end, because refugees end up as long-term or even permanent residents in different cultural and economic contexts. This triggers sudden changes in demand, e.g., for consumption, education, labor and housing. Furthermore, refugee flows can be a mechanism through which conflicts spread to neighboring countries (Salehyan & Gleditsch 2006). At the same time, migrants also form the base for a creative cosmopolitan, multicultural society and contribute to solving demographic challenges in aging societies. This range of factors shows that migration is not only intertwined with conflict but also with challenges of social cohesion in receiving countries.
Discrepancies in welfare between countries constitute another central variable of relevance, as relative deprivation theorists argue (Stark & Taylor 1989, Jennissen 2007). This theory would hence expect migration flows to run primarily from poor to rich regions and countries. Contrary to this proposition, however, empirical research shows that transnational mobility takes place primarily between countries that lie close to each other geographically – and not over long distances between the deprived Global South and the prosperous Global North (Deutschmann 2016). A likely explanation is that crossing distance is associated with costs and migrants and refugees are endowed with limited resource stocks. Furthermore, people may satisfy their needs to a gratifying extent at close locations despite potential additional gains at more distant locations (Stauffer 1940). It is thus necessary to take both physical distance and people’s personal characteristics (e.g., resource stocks) into account when modelling patterns of migration.
As this summary reveals, the individual decision to emigrate can be complex, depending on many individual and external factors. Even if the decision is made, there is still a variety of possible target destinations and several possible routes and modes of traveling. The traditional perspectives with their focus on push and pull factors are not able to shed light on individual decisions and choices between reasonable alternatives (e.g., possible routes, modes, and destinations) of objectively equal “value”. They neglect that individuals are to some extent active decision-makers, who are not isolated but embedded in relations with other individuals. Such networks can lead to chains of migration, i.e. cumulative migration patterns based on the transmission of information between transnational communities that often involve a specific village in the sending country and a particular neighborhood in the receiving country (Levitt 2001).
Computational Social Science can provide useful tools to take these two heavily neglected factors into account and to model migration flows dynamically. Agent-based models allow to simulate how individual actors (e.g., migrants) with certain characteristics (e.g., wealth or skill level) move through space (which can be modeled as furnished, for instance, with varying stocks or labor market opportunities or security levels) and how these individuals influence each other in doing so, e.g. by communicating (Epstein & Axtell 1996, Hedström 2006).
Such simulations could be used to describe how distance and resource endowment interact in structuring human migration. This has, however, not yet been done to date, a fact that often leads to false assumptions, as the Syrian case illustrates: In November 2015, economists argued that two thirds of Syrian refugees in Germany “can hardly read and write” (Wiarda 2015). This figure, however, stems from demographics of Syrians in Syria. The underlying assumption that it can simply be extrapolated to the population of fled Syrians in Germany is wrong, as very recent survey data reveals: interviews show that less than 3 percent of actual Syrian refugees in Germany have no formal education and that four fifths have more than elementary school education. 27 percent have a university degree and another 27 percent have finished the equivalent of “Gymnasium” (Rich 2016). The discrepancy between the former and the latter figures suggests that a highly selective sample of refugees arrives at relatively distant locations such as Germany which is much better equipped than the general population in the sender country. Combining demographic data with information on distance and country characteristics using agent-based models as described above will allow us to develop more realistic assumptions about the interaction between space and resources in structuring migration. Doing so may help equip host societies with more realistic information and scenarios that could contribute to developing more adequate integration policies and to assessing needs more precisely, even under conditions where knowledge about specific migrated individuals is scarce.
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