Impacts of COVID-19 on Public Universities in Brazil: A Machine Learning Counterfactual Analysis

Author:

Rossi R.

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

Subject

General Medicine

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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3