RATIO OF WOMEN EDUCATION IN PAKISTAN AT UNIVERSITY LEVEL

Abstract

Over the last decade Pakistan has made progress in expanding access to basic education; however, the transition to tertiary education continues to show gendered patterns that vary by province, urban/rural location and field of study. This paper examines the ratio of women at the university level in Pakistan using national administrative and survey sources (Higher Education Commission, Pakistan Education Statistics, UNESCO/ World Bank indicators) and recent empirical studies from 2019–2024. The study adopts a mixed-methods approach: quantitative trend and cohort analysis using HEC enrolment figures and UNESCO/WDI indicators, and qualitative synthesis of recent field studies addressing barriers faced by female students in marginalised regions. Key findings show that the female-to-male ratio in tertiary enrolment has improved over recent years and, in some years, reached parity at the aggregate level, but this masks large subnational disparities: women remain under-represented in STEM and technical fields and over-represented in arts/social sciences (HEC, 2023–24; PIE, 2024). Structural barriers — distance to institutions, safety and transportation, socio-cultural norms, and institutional capacity — still constrain female participation in many districts (UN Women, 2024; Habib, 2024). The analysis using SPSS and Excel for descriptive statistics and trend charts indicates a rising gross enrolment ratio (GER) for women in tertiary education (national GER ~11% in 2023; World Bank/UNESCO data) and a female: male tertiary enrolment ratio approaching or exceeding 1.0 in national aggregates in some recent years, but with high variance across provinces (HEC, 2023–24; UNESCO, 2023). The paper concludes with policy recommendations to increase equitable access — expanding women-friendly campuses, targeted scholarships, transport and safety measures, and promotion of women in STEM — and recommends future research on intersectional barriers using disaggregated longitudinal datasets.