Data-Driven Analysis of Wage Discrimination in Pakistan’s Higher Education Sector Using Logistic Regression

Abstract

Wage gaps are a stubborn fixture in many labour markets, particularly where gender norms can be seen to determine who works and how much they earn. In Pakistan’s universities and colleges, the demand for pay fairness has escalated, but systematic evidence about who is paid what remains thin. In order to plug this gap, we analyzed the latest Pakistan Labour Force Survey (2024‑25) using information‑technology-driven analytics for unravelling wage distribution curves in the higher‑education sector. We calculated the odds of receiving a lower salary relative to others while controlling for gender, highest education level, and years of experience using a logistic regression framework., and type of contract. The model shows that women are still at a disadvantage: even when accounting for qualifications and tenure, the chance that a woman earns less than an otherwise similar man is significantly lower. Education and experience do count; a postgraduate degree or more than ten years on the job attract higher pay: but the gender effect extends through all levels. An overview of our participation shows that the labour pool is male-dominated (68.7% of participants) with women representing a mere 22.7%. Unemployment paints a similar picture: 10.5 percent of women are unemployed, compared with 6.8 percent of men. This imbalance is not a statistical fluke — these numbers point to a structural inequality. This evidence suggests a clear need for targeted policy interventions and greater transparency from university administrations. Pakistan can work towards a fairer pay architecture for academics across gender lines, through tightening standards around reporting of wages as well as instituting mechanisms of monitoring.