GENDERED DETERMINANTS OF YOUTH UNEMPLOYMENT IN KENYA
Abstract
Purpose of the study: The study aimed at investigating the gendered determinants of youth unemployment in Kenya, focusing on how socio-economic, demographic, and institutional factors influence employment outcomes for young men and women.
Problem statement: Youth unemployment in Kenya has been on the rise from 5.15% in 1980 to 13.4% in 2024, with significant gender disparities. In 2023, 25.5% of young women were unemployed compared to 18.2% of young men. Female youth unemployment rates have consistently been higher than male youth unemployment rates from 1990 to 2024. The gender-specific dimensions of youth unemployment, however, remain under examined despite various policy interventions by the Kenyan government.
Methodology: The study employed a cross-sectional research design using data from the 2019 Kenya Population and Housing Census comprising a sample of 850,252 youth aged 18-34 years. The study applied a probit regression model to analyze the likelihood of unemployment based on gender-specific characteristics, with separate estimations for rural and urban areas.
Findings: The study found that age, education, skills, marital status, and residence significantly influence youth unemployment with clear gender and rural-urban differences. Returns to education diminished for young women at tertiary level (2.6%) compared to young men (4.2%), and at university level (2.5%) for young women relative to 3.7% for young men. Urban residence increased unemployment probability by 8.6% for young males and 14.8% for young females while skills training reduced unemployment rates for young males by more than double (3.2%) compared to young females (1.5%).
Conclusion: The study concluded that youth unemployment in Kenya is gendered and spatially differentiated. While female youth need targeted support, male youth should benefit from industry-linked school-to-work transition programmes. Long-term job creation could be boosted through labor-intensive sectors such as manufacturing, agribusiness, construction, and the digital economy.
Keywords: youth unemployment, gendered determinants, rural-urban disparities, Probit model.
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