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

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.

Automated summary

This study examines the impact of push-pull factors on migration intentions of potential migrants in northern Kazakhstan using Bayesian Networks. The findings suggest that policies restricting urban in-migration can be mitigated by factors such as social networks and reverse remittances. Providing accessible information on urban income and housing costs, as well as improving education resources, can help reduce migration intentions among rural residents.