Inequality and migration in Kenya: Investigating the subnational associations using census data

Background : Since the early 2000s, there has been an extensive debate on whether migration and inequality are interlinked, with varying conclusions arising from methodological as well as theoretical dispositions. The aim of this study is to contribute to this debate by exploring the nexus between several dimensions of inequalities and migration in Kenya. Methods : This study used the subnational(county) data on inequalities and migration in Kenya obtained from several reports. Four explanatory variables including access to water, electricity, composite index of County Human Development Index (County HDI) and County Gini were used. Our dependent variable was migration intensity, measured by the Revised Weighted Net Migration Rate. Correlation and spatial regression analysis were performed to model the relationship between migration and inequality.   Results : Findings revealed that migration had a non-linear relationship with income inequality, such that a change in one unit of migration intensity results in a 567 negative change in County Gini. The County Gini had the highest explanatory power in our model, although counties with high HDI tend to have higher migration intensities. Migration intensities in the country were not randomly distributed as we found evidence of spatial clustering with two key emergent hotspots, a high-high in the lake region and a low-low in the coastal region. Regions with low migration intensities correspond with higher poverty, implying that structural factors may explain the migration intensities in the country.   Conclusions : The study highlights that the subnational income inequality reduces as migration intensifies. We conclude that migration has an equalizing effect on inequality as observed in some studies. Regions with high poverty tend to have lower migration intensity, implying that structural factors are important in influencing migration. Use of migration intensity and application of spatial analysis have improved our understanding of migration and inequality, and should be applied in future research.