Predicting International Student Enrollment by Institutional Aid: A Random and Fixed Effects Approach

Abstract

Since the fall semester of 2016, first-time international student enrollment (ISEft) has declined at U.S. colleges and universities. This trend disrupts a steady upwards trajectory of ISEft rates. Previous research has demonstrated that various political, social, and macroeconomic factors influence the number of international students studying in the U.S. Exploiting data from the Common Data Set (CDS), I focus on the role financial aid plays as an enrollment predictor for international undergraduate students. A fixed effects model reveals that financial aid is strongly and significantly predictive of ISEft, yielding a 1.8% enrollment increase per 10% aid increase, all else equal. Interestingly, financial aid is only predictive of ISEft if it is awarded in substantial amounts. Extending the work of Bicak and Taylor (2020), I also analyze how the effectiveness of financial aid awards varies within different institutional settings. Random effects regressions reveal that rural, low research, and private universities experience considerable marginal ISEft boosts when awarding aid to international students. The findings of this work are primarily directed at institutional leaders who seek to revitalize their institution’s ISEft policy. Moreover, these insights may inform local policymakers who seek to incent ISEft.