University Admission: Lottery or Selection, Which Boosts Gender Diversity?

In recent years universities have tried different ways to pick students, especially when spots are limited. A Dutch team compared two systems: weighted lottery, where everyone gets a chance proportional to their score, and traditional selection, based on academic criteria. They examined over 2,000 applicants to a biomedical sciences program, looking at gender balance and study success. The lottery attracted more women, creating a more natural gender balance. In contrast, selection saw fewer men apply, but those admitted performed better in the first year and finished within four years. However, these outcomes depend more on personal factors – age, high‑school GPA, motivation – than on the admission method itself. In practice, lottery seems to boost diversity, while selection links to higher performance, but doesn’t guarantee gender equity. The authors suggest mixing both approaches to achieve inclusivity and academic excellence.

The statistical analysis employed logistic regression models to assess the impact of the admission procedure on academic outcomes, controlling for covariates such as age, pre‑university GPA, and declared motivation. Results show that once these factors are adjusted, the success difference between the two groups is no longer significant (p > 0.05). This indicates that selection does not add predictive value beyond the already known student characteristics. Moreover, the application distribution revealed a gender bias at the application stage: men were under‑represented in the selection applicant pool, likely due to cultural perceptions of biomedical pathways. Weighted lottery, however, maintained a more equitable proportion, suggesting that transparency and perceived fairness of the process can influence the decision to apply. From a policy perspective, a possible solution is to introduce a hybrid system: a minimum quota of spots allocated via lottery to ensure diversity, combined with merit‑based selection for the remaining spots, thus optimizing both equity and academic performance.