This study investigates how self-assessments of programming skills influence gendered career preferences in the tech sector. While women remain underrepresented in high-quality tech positions, existing research suggests that this disparity is shaped not only by external hiring biases but also by self-selection processes. Women are more likely to opt out of technology-intensive study fields, drop out at higher rates, and shift career paths despite having similar qualifications as their male counterparts. This pattern raises the question of whether biased self-perceptions of programming ability contribute to these decisions.
To explore this, the study focuses on students at top Swiss universities, a highly selective group with strong prospects at major tech firms. Switzerland provides an ideal case, as it has one of the largest ICT workforces in Europe yet remains a highly male-dominated sector. The study employs a choice experiment embedded in a broader survey, where respondents evaluate hypothetical job offers with systematically varied attributes, such as competition to get in, team orientation, company size, gender composition, coding pressure and performance expectations.
Additionally, participants will complete a short programming test designed to reflect hiring assessments commonly used in industry. They will then be asked to evaluate their own performance without knowing their actual scores. This approach enables an objective analysis of biases in self-assessment and their impact on career choices. By integrating experimental and survey data, the study aims to better understand how gendered perceptions of competence contribute to women’s underrepresentation in prestigious tech roles.