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Background <p>The waiting time to obtain the first employment varies with the socio-economic factors and the individual characteristics of a graduate. This paper estimated the average waiting time to first employment using survival models.</p> Methods <p>The study used data from 2020 to 2021 Mulungushi University graduate tracer survey, 276 graduates. Data were collected using structured questionnaires and analyzed using descriptive statistics, Kaplan–Meier survival estimates, Cox proportional hazards models, and log-logistic accelerated failure time (AFT) models.</p> Results <p>Results show that 52.5% of graduates were employed. Employment outcomes significantly vary by age, gender, and school of study (<i>p</i> &lt; 0.05), with higher employment rates among older graduates and males. The School of Medicine and Health Sciences recorded the highest employment rate (86.4%), while the School of Social Sciences recorded the lowest (29.4%). The median waiting time to first employment was 19 months, with substantial variation across groups. Kaplan–Meier results show shorter unemployment durations among older graduates, males, medical-related fields, and higher academic performers. The log-logistic AFT model identifies age, gender, and school of study as significant determinants of employment duration. Graduates aged 30 and above transition faster (TR = 0.22, p = 0.003), while females experience longer waiting times (TR = 1.64, <i>p</i> &lt; 0.10). Graduates from Social Sciences also face longer delays (TR = 5.55, <i>p</i> &lt; 0.10). Degree classification and internship experience are not statistically significant in the multivariate model. w that 52.5% of graduates were employed. Employment outcomes significantly vary by age, gender, and school of study (<i>p</i> &lt; 0.05), with higher employment rates among older graduates and males. The School of Medicine and Health Sciences recorded the highest employment rate (86.4%), while the School of Social Sciences recorded the lowest (29.4%). The median waiting time to first employment was 19 months, with substantial variation across groups. Kaplan–Meier results show shorter unemployment durations among older graduates, males, medical-related fields, and higher academic performers. The log-logistic AFT model identifies age, gender, and school of study as significant determinants of employment duration. Graduates aged 30 and above transition faster (TR = 0.22, <i>p</i> = 0.003), while females experience longer waiting times (TR = 1.64, p &lt; 0.10). Graduates from Social Sciences also face longer delays (TR = 5.55, <i>p</i> &lt; 0.10). Degree classification and internship experience are not statistically significant in the multivariate model.</p> Conclusion <p>Employment transitions are primarily influenced by age, gender, and field of study. The findings highlight heterogeneous labour-market entry patterns and the need for targeted employment policies to improve graduate outcomes.</p>

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Survival analysis of time to first employment among university graduates in Zambia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月13日

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  • Teza Sinyangwe,
  • Yordanos Gebremeskel,
  • James Mulenga,
  • Herbert Tato Nyirenda

摘要

Background

The waiting time to obtain the first employment varies with the socio-economic factors and the individual characteristics of a graduate. This paper estimated the average waiting time to first employment using survival models.

Methods

The study used data from 2020 to 2021 Mulungushi University graduate tracer survey, 276 graduates. Data were collected using structured questionnaires and analyzed using descriptive statistics, Kaplan–Meier survival estimates, Cox proportional hazards models, and log-logistic accelerated failure time (AFT) models.

Results

Results show that 52.5% of graduates were employed. Employment outcomes significantly vary by age, gender, and school of study (p < 0.05), with higher employment rates among older graduates and males. The School of Medicine and Health Sciences recorded the highest employment rate (86.4%), while the School of Social Sciences recorded the lowest (29.4%). The median waiting time to first employment was 19 months, with substantial variation across groups. Kaplan–Meier results show shorter unemployment durations among older graduates, males, medical-related fields, and higher academic performers. The log-logistic AFT model identifies age, gender, and school of study as significant determinants of employment duration. Graduates aged 30 and above transition faster (TR = 0.22, p = 0.003), while females experience longer waiting times (TR = 1.64, p < 0.10). Graduates from Social Sciences also face longer delays (TR = 5.55, p < 0.10). Degree classification and internship experience are not statistically significant in the multivariate model. w that 52.5% of graduates were employed. Employment outcomes significantly vary by age, gender, and school of study (p < 0.05), with higher employment rates among older graduates and males. The School of Medicine and Health Sciences recorded the highest employment rate (86.4%), while the School of Social Sciences recorded the lowest (29.4%). The median waiting time to first employment was 19 months, with substantial variation across groups. Kaplan–Meier results show shorter unemployment durations among older graduates, males, medical-related fields, and higher academic performers. The log-logistic AFT model identifies age, gender, and school of study as significant determinants of employment duration. Graduates aged 30 and above transition faster (TR = 0.22, p = 0.003), while females experience longer waiting times (TR = 1.64, p < 0.10). Graduates from Social Sciences also face longer delays (TR = 5.55, p < 0.10). Degree classification and internship experience are not statistically significant in the multivariate model.

Conclusion

Employment transitions are primarily influenced by age, gender, and field of study. The findings highlight heterogeneous labour-market entry patterns and the need for targeted employment policies to improve graduate outcomes.