Global pattern in hunger and educational opportunity: a multilevel analysis of child hunger and TIMSS mathematics achievement
-
Published:2023-04-11
Issue:1
Volume:11
Page:
-
ISSN:2196-0739
-
Container-title:Large-scale Assessments in Education
-
language:en
-
Short-container-title:Large-scale Assess Educ
Author:
Canbolat YusufORCID, Rutkowski David, Rutkowski Leslie
Abstract
AbstractIn low-income countries, there exists a common concern about the effect of hunger and food insecurity on educational outcomes. However, income inequalities, economic slowdown, conflict, and climate change have raised those concerns globally. Yet, little is known about how widespread the problem of hunger in schools is worldwide. This study examines child hunger and student achievement internationally, using data from the Trends in Mathematics and Science Study (TIMSS) 2019. To examine the relationship between hunger and student achievement, we fitted multilevel models to the data and controlled for student SES, class SES, teacher experience, and teacher educational attainment. The results suggest that hunger among students is not exclusive to low-income countries. Instead, child hunger is a common issue around the world, affecting about one-third of children and exacerbating unequal education opportunities globally. Controlling for other variables, the achievement gap between students who never come to school hungry and those who come to school always or almost always hungry is significant and deserves our attention. A clear policy recommendation from our results suggests that all countries that participated in TIMSS need to examine their school meal programs and explore ways to feed the students who show up to school hungry.
Publisher
Springer Science and Business Media LLC
Reference52 articles.
1. Alderman, H., Hoddinott, J., & Kinsey, B. (2006). Long term consequences of early childhood malnutrition. Oxford Economic Papers, 58(3), 450–474. 2. Angrist, N., Evans, D. K., Filmer, D., Glennerster, R., Rogers, F. H., & Sabarwal, S. (2020). How to improve education outcomes most efficiently? A comparison of 150 interventions using the new learning-adjusted years of schooling metric. The World Bank. https://doi.org/10.1596/1813-9450-9450 3. Asparouhov, T. (2006). General multi-level modeling with sampling weights. Communications in Statistics Theory and Methods. https://doi.org/10.1080/03610920500476598 4. Aurino, E., Gelli, A., Adamba, C., Osei-Akoto, I., & Alderman, H. (2020). Food for thought? experimental evidence on the learning impacts of a large-scale school feeding program. Journal of Human Resources, 11, 1123. 5. Bailey, P., Emad, A., Huo, H., Lee, M., Liao, Y., Lishinski, A., Nguyen, T., Xie, Q., Yu, J., Zhang, T., Buehler, E., Bundsgaard, J., C’deBaca, R., & Christensen, A. A. (2021). EdSurvey: analysis of NCES education survey and assessment data (2.7.1). Computer software. https://CRAN.R-project.org/package=EdSurvey
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|