Inferring migrant characteristics from a minimalistic model: a case study of South Sudan
Abstract
Migration decisions are driven by numerous factors, from living conditions at origins to economic opportunities and ethnic enclaves at destinations. Additionally, priorities of migrants can shift over time as a migration episode unfolds. Here we developed a parsimonious Markov chain model that captures the effects of the changing mindset of migrants (from safety concerns to economic decisions), the benefit of agglomeration, social ties, the environment, and distance. The model was then implemented for refugees in South Sudan, with a focus on the populations of ten refugee camps located in eastern Africa in a two-year period (Dec 2013 - Dec 2015). A Monte Carlo Markov Chain (MCMC) analysis was carried out to infer the model parameters that characterize the migrants. We consider several key questions that are relevant for our model: How much more do migrants value people from the same cultural background, compared to those from different backgrounds, in deciding a destination choice? How long may the impact of conflicts on the refugees migration decisions last? The corresponding quantities are critical for migration process but are not easily measured. Here, we inferred their values with the help of the model and MCMC. Preliminary results are reported and potential extensions of the model discussed.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMGC45P..09S