Abstract
This study delves into the impact of the COVID-19 pandemic on the enrollment rates of on-site undergraduate programs within Brazilian public universities. Employing the Machine Learning Control Method, a counterfactual scenario was constructed in which the pandemic did not occur. By contrasting this hypothetical scenario with real-world data on new entrants, a variable was defined to characterize the impact of the COVID-19 pandemic on on-site undergraduate programs at Brazilian public universities. This variable reveals that the impact factor varies significantly when considering the geographical locations of the institutions offering these courses. Courses offered by institutions located in smaller population cities experienced a more pronounced impact compared to those situated in larger urban centers.
Publisher
School of Statistics, Renmin University of China
Reference19 articles.
1. Matrix completion methods for causal panel data models;Journal of the American Statistical Association,2021
2. Ensemble methods for causal effects in panel data settings;American Economic Association Papers and Proceedings,2019
3. Recursive partitioning for heterogeneous causal effects;Proceedings of the National Academy of Sciences of the United States of America,2016
4. Program evaluation and causal inference with high-dimensional data;Econometrica,2017