Immigration and Unemployment Nexus: A Micro-Level Investigation of Ugandan Youth

Immigration and Unemployment Nexus: A Micro-Level Investigation of Ugandan Youth

Esra Karapınar Kocağ
DOI: 10.4018/978-1-6684-6750-3.ch006
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Abstract

Youth unemployment is a rising concern for many countries across the world. The gap between youth and adult is even wider in Sub-Saharan Africa than the world average. There might be several reasons to explain, yet this chapter focuses on one controversial potential reason: immigration. This continent has experienced considerable migration flows and one could expect that immigration worsens labour market conditions for native youth. Uganda as one of the Sub-Saharan countries is investigated to see if immigrants have a significant impact on unemployment probability of young Ugandans using cross-sectional census data for the years of 1991, 2002, and 2014. Data set was drawn from IPUMS-International. Findings indicate that regional share of immigrants does not have a significant large effect on unemployment probability of youth in Uganda. A further investigation showed that higher share of immigrants in a given region lowers the probability of being not in the labour force across specifications. This means immigrants do not push native youth out of the labour force in the Uganda case.
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Background

Concerns and discussions about youth unemployment are not a recent issue for countries all over the world because of the gap between youth and adult unemployment. According to the ILO Statistics (2020), unemployment rate for people over 15 years old was 4 per cent of the total labour force, while it was 13.5 per cent for people in the age group of 15-24 in 2019.

Key Terms in this Chapter

Youth Unemployment: Youth unemployment is the case of young people, whose age is between 15–24 years as defined by international organisations such as the United Nations, are searching for a job, but cannot find a job.

Probit Model: This is regression model in which the dependent variable is binary. This means dependent variable takes only two values such as “1” for unemployed, and “0” for employed.

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