2018
DOI: 10.25079/ukhjss.v2n2y2018.pp1-10
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An Analysis of the Determinants of Youth Employment in Rwanda

Abstract: The main objective of this research is to analyze the determinants of youth employment in Rwanda from the point of view of the demand, supply and the general labor market. An analysis of the data shows that a skill gap is most critical for employment creation and a transition from school-to-work seems problematic. Further, questions remain about what factors influence youth employment in Rwanda and how youth employment is related to poverty reduction and distribution of income. The study uses a multinomial log… Show more

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Cited by 7 publications
(8 citation statements)
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“…The study's findings show that gender, geographical location, education, skills, and marital status are all significant factors in explaining the difference in youth unemployment in Tanzania. In Africa also, Ndagijimana et al (2018) shed light on the determinants of youth employment status in Rwanda using the multinomial logit model. after analysis of the data used, the study suggested that youth employment in Rwanda is influenced by gender, age, education, and geographical location and based on its findings it provided policy recommendations to promote youth employment.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The study's findings show that gender, geographical location, education, skills, and marital status are all significant factors in explaining the difference in youth unemployment in Tanzania. In Africa also, Ndagijimana et al (2018) shed light on the determinants of youth employment status in Rwanda using the multinomial logit model. after analysis of the data used, the study suggested that youth employment in Rwanda is influenced by gender, age, education, and geographical location and based on its findings it provided policy recommendations to promote youth employment.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Access to credit was quite low among the respondents in Kenya and Nigeria (21.75% and 20.98%, respectively), which could be attributed to the lack of creditworthiness among young people (Buszko et al 2020 ; Ndagijimana et al 2018 ). However, contrary to many studies conducted in Uganda (Bukuluki et al 2020 ; Mulume et al 2022 ), more respondents in Uganda (65%) had access to credit.…”
Section: Methodsmentioning
confidence: 99%
“…Lower education level limits women-owned SMEs in Kigali to small-sized businesses. The observed relationship between education and level of business was reported by [36]. Graduate entrepreneurs are more likely to run small and medium businesses than the less educated ones who concentred on micro-enterprises.…”
Section: Hiv/aids Challengementioning
confidence: 96%