Analysis of employment factors for university graduates in Kazakhstan

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Having a university degree provides only a slight advantage over those without higher education, making other factors crucial in determining a graduate’s employment prospects. This study aims to analyze the factors that affect the employment of university graduates and define opportunities for public administration and university management in Kazakhstan. A logistic regression model based on primary data was used to examine the impact of the availability of practical experience and jobs in their specialty, social connections, technology development, and entrepreneurship on the likelihood of employment. The survey was conducted in 2022 and involved 300 graduates of the 2020–2021 academic year from all regions of Kazakhstan. Findings show that personal connections (F3) and technology and entrepreneurship (F4) positively impact graduates’ job prospects. However, lack of experience (F1) and the limited number of job offers (F2) reduce employment likelihood. If a graduate responds with 3 points for F1, 7 for F2, 7 for F3, and 1 for F4, they are less likely to secure a job in their specialty within a year of graduation. The main practical value of this study is that university career centers can use this model to predict the likelihood of graduates being employed. Providing a sophisticated online platform and different analytics-driven career services, open access to administrative data on the labor market, and new programs for students’ job experience and entrepreneurship will prepare university graduates for a dynamic labor market and reduce the mismatch between education and employment needs.

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    • Figure 1. Classification graph
    • Тable 1. Logistic regression model
    • Table 2. Model classification
    • Conceptualization
      Aigerim Sekerbayeva, Saltanat Tamenova, Bulent Tarman, Dina Razakova, Zaira Satpayeva
    • Data curation
      Aigerim Sekerbayeva
    • Formal Analysis
      Aigerim Sekerbayeva
    • Investigation
      Aigerim Sekerbayeva, Zaira Satpayeva
    • Methodology
      Aigerim Sekerbayeva
    • Project administration
      Aigerim Sekerbayeva, Zaira Satpayeva
    • Software
      Aigerim Sekerbayeva
    • Validation
      Aigerim Sekerbayeva, Saltanat Tamenova, Bulent Tarman
    • Visualization
      Aigerim Sekerbayeva, Zaira Satpayeva
    • Writing – original draft
      Aigerim Sekerbayeva, Saltanat Tamenova, Bulent Tarman, Dina Razakova, Zaira Satpayeva
    • Writing – review & editing
      Aigerim Sekerbayeva, Saltanat Tamenova, Bulent Tarman, Dina Razakova, Zaira Satpayeva
    • Resources
      Saltanat Tamenova, Dina Razakova
    • Supervision
      Bulent Tarman
    • Funding acquisition
      Dina Razakova