Effects of STEM Education on Unemployment Types: An Applied Science Case Study

Rapid technological transformation and digitalization have reshaped labor markets by changing skill demands and affecting various types of unemployment. In this context, STEM (Science, Technology, Engineering, and Mathematics) education has gained prominence as a means of equipping individuals with 21st-century skills and improving employability. This study investigates teachers’ perceptions of STEM education’s impact on different unemployment types. A total of 45 teachers were selected through criterion sampling, and data were collected using a semi-structured interview form and analyzed with inductive content analysis within a structured qualitative design. Findings indicate that teachers view STEM education as reducing multiple unemployment types, especially involuntary, frictional, cyclical, structural, hidden, technological, and seasonal unemployment, by enhancing qualified human capital, fostering interdisciplinary skills, promoting entrepreneurship and innovation, and supporting adaptability and lifelong learning. However, participants noted its limited effect on natural and real wage unemployment. Expectations of higher wage demands and labor substitution by technology suggest STEM education may not significantly reduce voluntary or real wage unemployment and could even increase technological unemployment. Overall, STEM education is regarded as a strategic tool for improving employment outcomes, although its effectiveness depends on economic conditions and public policies, highlighting the need for future research using mixed methods and multiple stakeholders to better understand its relationship with labor market dynamics.