Educational attainment and the likelihood of unemployment among youth in Kenya

• In Event: Broken Promises - Youth and the Benefits of Schooling

Proposal

Globally, in 2020, more than one in five (22.4 per cent, in total about 282 million) youths aged 15–24 were neither in employment, education or training (ILO, 2022). Young individuals experiencing these conditions have lost an essential period of their initial personal and career development and growth. Also, the extended period of joblessness during one's youth can carry lasting adverse consequences such as diminished future earnings, limited career opportunities, and an increased probability of facing unemployment later, reinforcing a cycle of poverty and obstructing upward social mobility. Moreover, elevated levels of youth unemployment can cause a decline in productivity and potential economic output.

While a high percentage of youth who are in unemployment or education is a common problem across many countries, Kenya is a particularly important place to investigate this topic due to its young and growing population. In 2019, Kenya had a population of 47.6 million and about 68 percent of its population was younger than 30 years old (CBS, 2019). Among those under 30 about 58percent of them were below the age of 15 years. As this group of youth will be joining the labor force within the next two decades, it is expected that demand for job opportunities will grow substantially. These trends underlie challenges Kenya faces as its economic prosperity hinges on its ability to address this increasing demand for jobs among its youth.

Nevertheless, the youth labor market in Kenya is marked by a high level of unemployment. In 2022, about 13 percent of Kenyan youth aged between 15-24 remained unemployed according to the ILO data, a significant increase compared to 7.4 percent in 2016. It is important to note that these statistics failed to show a large percentage of Keyan youth working in the insecure and low-paying informal Jua Kali sector. To address these challenges facing the youth, the Kenyan Government in collaboration with the private sector and development partners have developed policies and implemented programs aimed at reducing youth unemployment. Despite these policies and efforts to increase youth employment, there is still a marked lack of evidence on factors affecting youth unemployment in Kenya.

In this paper, we address this gap by identifying determinants of unemployment among youth aged between 18 and 24 years in Kenya using the 2009, 2014, and 2022 Kenya Demographic and Health Survey (KDHS). We are particularly interested in exploring the role of educational attainment in improving youth employment. The study mainly applies logistic regression model to examine the relationship between youth employment and educational attainment. The average educational level of attainment among youth in Kenya has significantly increased over the last two decades; the percentage of youth enrolled in higher education more than doubled during this period, and secondary enrollment increased over 50 percent (World Bank, 2022). This increased overall education level among youth does not necessarily lead to better labor market outcomes because the effects of education on one’s employment are mediated by other factors beyond education, such as the condition of economy and changes in demographics. Through descriptive analysis, we further explore how educational attainment has interacted with the condition of the Kenyan labor market, determining youth employment.

We find evidence suggesting completing secondary education increases one’s probability of being unemployed after taking into account individual, household, and other structural factors including ethnicity and religion, which is consistent with the findings of recent studies that reported a negative or null association between one’s employment and a higher level of education (Chudgar, Kim, Morley, & Sakamoto, 2019; Egessa, Nnyanzi, & Muwanga, 2021; Nganwa, Assefa, & Mbaka, 2015). Additionally, we document that mismatch between supply of and demand for education possibly explain this counterintuitive finding and discuss how future polices can address education and occupation mismatch.

References

Central Bureau of Statistics (CBS). (2019). Kenya Population and Housing Census 2019 Analytical Report Vol.III. Office of The President and Ministry of Planning and National Development.
Chudgar, A., Kim, Y., Morley, A., & Sakamoto, J. (2019). Association between completing secondary education and adulthood outcomes in Kenya, Nigeria, Tanzania and Uganda. International Journal of Educational Development, 68, 35–44.
Egessa, A., Nnyanzi, J. B., & Muwanga, J. (3921). Determinants of youth unemployment in Uganda: The role of gender, education, residence, and age. IZA Journal of Labor Policy, 11(1). https://doi.org/doi:10.2478/izajolp-2021-0008
International Labour Office (ILO). (2022). World Employment and Social Outlook: Trends 2022.
Nganwa, P., Assefa, D., & Mbaka, P. (2015). The Nature and Determinants of Urban Youth Unemployment in Ethiopia. Public Policy and Administrative Research, 5(3), 197–207
World Bank. (2022). Kenya Economic Update, June 2021: Rising above the wave. World Bank Group.

Authors

• Youngran Kim, Michigan State university

• Leapetswe Malete, Michigan State University

• Stephanie Irene Winston, Michigan State University