Impact of COVID-19 Lockdowns on Learning Losses : Structural and Individual Factors

The COVID-19 pandemic has wrought unprecedented disruptions on global education systems, highlighting significant educational learning losses and exposing deep-rooted inequalities. This thesis investigates the impact of structural and individual factors on learning losses during the pandemic, utilizing regression analyses and international databases to dissect the differential effects across income classifications of countries. Specifically, the research examines how internet access, the quality and prevalence of private schools, government educational spending, and household income are associated with learning outcomes during lockdown periods. The study shows that for low- and middle-income countries, traditional factors such as internet penetration and quality of schooling do not significantly explain learning losses, suggesting an extreme magnitude of challenges and systemic issues like political instability and pre-existing educational inequities. Interestingly, household income emerges as a critical determinant, aligning with the theoretical underpinnings of capability theory and the digital divide framework discussed in the thesis. In contrast, high-income countries see private school prevalence and quality as significant factors, with findings indicating that private schools' agility and resource availability played a pivotal role in mitigating learning losses. This thesis underscores the complex interplay between macro and micro-level factors affecting learning during the pandemic. It contributes to a deeper understanding of the pandemic's educational ramifications and the pressing need for context-specific solutions to ensure inclusive and equitable learning opportunities for all students, regardless of their socioeconomic background.