Exploring Policy Options in Regulating Rural–Urban Migration with a Bayesian Network: A Case Study in Kazakhstan

Abstract

Despite the benefits associated with the free movement of people, governments often try to regulate urban immigration by constraining the agency of potential rural out-migrants in moving to cities and/or in expanding their agency to enable them to stay put. We apply an institutional framework centring on push–pull and retain–repel factors to migration intentions of potential migrants in northern Kazakhstan. We model the effects of these factors on migration intentions with Bayesian Networks and expand the baseline model with three policy scenarios. The results suggest that the effects of policies constraining urban in-migration, e.g. limiting access to affordable housing, are attenuated by social networks and reverse remittances. The supply of accessible and appropriate information on possible income and true housing costs in urban areas presents a promising road to reduce intentions of rural out-migration. Better schools and decentralised tertiary education can also reduce the migration intentions of rural residents.

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Untitled

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