Global Change Institute (GCI)

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    Variation by Geographic Scale in the Migration-Environment Association: Evidence from Rural South Africa
    (Federal Institute for Population Research, 2017) Hunter, L.M.; Leyk, S.; Maclaurin, G.J.; Nawrotzki, R.; Twine, W.; Erasmus, B.F.N.; Collinson, M.
    Scholarly understanding of human migration’s environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around “climate migrants”. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such “natural capital” is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the “environmental surplus” hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this “operational scale sensitivity”. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability.