Using a modified network modeling approach, a team specifically examined water availability, temperature extremes, and socioeconomic statuses of migrants during two migration periods in South Africa. They found that, in many cases, residents were likely to leave areas struggling with climate instability, and regions with more reliable climates were more likely to attract migrants.
Some of the biggest differences in migration have been observed between those moving to urban areas or non-urban areas, such as farmland. Migrants moving to urban areas often left places with heavy rain – likely moving to escape urban flooding. However, people who moved to non-urban areas left places with excessive heat, which could indicate temperature-sensitive livelihoods through agriculture, the researchers noted.
Socio-economic factors also seemed to play a role. Some migrants settling in urban areas were particularly motivated by the country’s fluctuating unemployment rate. Non-urban migrants seemed to be particularly influenced by external factors that were not explicitly modelled, such as the abolition of apartheid policies in the late 1990s.
The results, published in Population and Environment, show the advantages of using network modeling – which is often not used in this context – to study migration. Using the model, the research team was able to study a geographic network of districts, rather than just individual migration.
“This type of model presents a promising method for conceptualizing and analyzing migration flows,” said Michael Oppenheimer, Albert G. Milbank Professor of Geosciences and International Affairs at the High Meadows Environmental Institute. “We believe it could be applied to other migration cases, different types of network flows, and conflict analyzes in a variety of contexts, including the projection of climate migration in a warmer world. .
The study was led by Tingyin Xiao, associate researcher, under the supervision of Oppenheimer.
“South Africa is expected to experience large increases in temperature averages, changes in rainfall patterns and further exacerbation of extreme water scarcity. It also has a particularly high rate of internal migration, which is why we chose it as the focus of our study,” said Xiao, who is based at the Center for Energy and Environmental Policy Research at the Princeton School of Public. and International Affairs. .
Xiao and Oppenheimer conducted the study with Xiaogang He, who was a graduate researcher at the High Meadows Environmental Institute – Science, Technology, and Environmental Policy (PEI-STEP) at Princeton and now an assistant professor at the National University of Singapore; and Marina Mastrorillo, economist at the Food and Agriculture Organization of the United Nations.
The team collected migration and socio-economic information from community surveys and censuses provided by Statistics South Africa. They looked at the number of adults aged 15 to 64 who moved from one district to another in five years. The periods studied are between 1997 to 2001 and 2007 to 2011. These periods were chosen according to the available data and also because these periods are post-apartheid, which may have played a role in migration at the time. .
The migrants were then divided into two groups: those who moved to an urban destination and those who moved to non-urban locations. Researchers then observed the effects of long-term water availability reflecting flow and reservoir levels, excessive heat, rainy season water deficit, and soil moisture conditions on migration to through South Africa.
Previous studies focusing on migration flows mainly use a “gravity model” to predict and examine migration flows. However, this type of model, according to the authors, has many limitations and can lead to biased estimates and results. Instead, the researchers modified an existing network model that had not previously been applied in the context of migration to analyze the influence of each factor on South African internal migration. This allowed the team to examine and observe many factors involved in inland migration and predict their relationships. They were able to identify travel patterns and climate-specific differences for urban and non-urban migrants, examine socio-economic influences, and compare previous migration trends.
The team also determined that earlier migration flows influence subsequent migration. Former migrants can establish links between the places from which they moved and to which they moved, which further facilitates future migration between these places. In some cases, past movement patterns may weaken the associations researchers have found between climate change and South African migration. Yet every migration trend inevitably depends on various climatic, destination, socio-economic and historical factors. Specific findings should not be generalized without considering these contextual factors, the researchers warn.
The authors believe that this study successfully fills the gaps of previous ones. It improves understanding of the impact of climate on migration and indicates the need to prepare more humane and effective migration policies in countries experiencing extreme climatic conditions or likely to receive migrants. They argue for further research on environmental migration with the use of network models.