Migration and Inequality in Kenya

Abstract

Since early 2000, understanding the nature of the migration and inequality relationship has received global attention. This arises from the different conclusions from research, on the nature of the association, as a result of conflicting conceptual and methodological dispositions. While most studies focused on the effect of migrant remittances on inequality only a few compare the counter effect of migration on inequality. In Kenya, earlier studies had indicated a possible relationship between the county migration patterns and the patterns of development. This study seeks get a deeper understanding of the relationship between inequality and subnational migration patterns in Kenya. Data were derived from Kenya Population and Housing census for 1999 and 2009, to reconstruct migration movements within the counties. The migration intensity measures for the 2009 census were used to generate the migration intensities for the analysis. Inequality data was obtained using four variables measured at the County level – access to water, access to electricity, the County Human Development Index and the County Gini, that measures income distribution. The test of the relationship was done using spatial analysis based on ArcGIS version 10.5. The findings show that migration has a significant relationship to the County Gini and County Human Development Index, although they influence each other differently. County Gini has a non-linear relation to the County migration intensity, but the County development measured by County HDI, exhibited a positive relationship with migration. The relationship between county migration intensity and County Gini was statistically significant, such that a unit change in migration intensity resulted in 567 negative unit change in County Gini. This finding corroborates previous studies that found a negative relationship between migration and income inequality. The finding leads to the conclusion that as migration intensifies, income inequalities reduce.

Several other findings from the study confirm that migration in Kenya is patterned such that regions with similar intensities neighbor each other. Two migration hotpots emerge in the analysis, one of high-high intensities in the western part of the country around the Lake Victoria basin, and low-low intensities, in the eastern part of the country in Mombasa and surrounding regions. The locations associated with low migration intensities are characterized by high poverty levels, which point to deeper systemic factors accounting for the migration patterns observed therein. Furthermore, the study findings show that Kenya has experienced changing patterns of migration between 1999-2009, notably the increased feminization observed across the years although internal migrants are moving shorter distances. The study recommends the use of spatial analysis to enrich future studies of migration dynamics in Kenya. It also recommends the use of qualitative methods to understand migration and inequality dynamics, to complement this quantitative study. Such a mixed methodology approach will nuance how migration affects intrahousehold inequalities beyond the income dimension. A recommendation for policy makers is to initiate a migration survey, that would enable analysis to be done at micro level – allowing for household and individual migrant characteristics to be tracked over time. This is a rallying call to support the push for a specialist migration survey to support future migration studies in the country