Women and men in tech: Invisible barriers, gendered self-perception, and the social reshaping of work

The persistent gender wage gap remains a pressing issue with far-reaching societal implications. Despite policy interventions aimed at closing this gap, the psychological and behavioral factors shaping wage expectations have received less attention. Drawing on the theory of statistical discrimination and signaling perspective, this study shifts the focus from employer-side discrimination to the self-perception of jobseekers, examining how gender-based biases contribute to wage disparities and reinforcing broader patterns of economic inequality. By analyzing a dataset of over 152,000 CVs from a job board in Eastern Europe, we explore how wage expectations are shaped by professional qualifications and internalized biases in self-assessment. Using the Blinder-Oaxaca decomposition method, we disentangle the wage gap into components related to technical skills and self-perception, revealing that women's lower wage expectations stem from their own interpretations of their education and work experience. While technical competencies, such as programming skills, are often assumed to level the playing field, our findings challenge this notion. In most cases, proficiency in coding does not alter wage expectations, suggesting that skills alone cannot dismantle ingrained gendered perceptions of worth. However, within the IT sector, mastery of programming languages contributes to what can be described as self-imposed wage penalties. This paradox underscores the complex interplay between skill recognition, and the persistent undervaluation of women's labor, ultimately reinforcing broader gendered divisions in the digital economy. This study highlights the need for deeper structural changes in policy and societal narratives around gender, self-perception, and economic value in the tech labor market. • Women undervalue their skills, contributing more to the gender wage gap than men. • Technical skills like C and JAVA show significant gender disparities in self-assessment. • Blinder-Oaxaca decomposition reveals self-discrimination as a key wage gap driver. • Verifiable technical skills can mitigate wage disparities by reducing information asymmetry. • Policy interventions should address self-perception biases to close the gender wage gap.