[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113222-en":3,"doc-seo-113222-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113222,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Decomposing Learning Inequalities in East Africa - How Much Does Sorting Matter?","Inequalities in learning opportunities emerge from both household- and school-related influences. Because these influences may interact rather than operate independently, sorting between schools and households could intensify educational gaps, yet it has been rarely quantified. The article introduces a novel variance decomposition approach and applies it to data from over one million East African children. Sorting explains about 8% of test-score variance, comparable to school-quality differences, and simulations suggest long-run inequality reductions if sorting is mitigated.","Pub lic Disclosure Authorized Pub lic Disclosure Authorized  \nThe World Bank Economic Review, 36(1), 2022, 219–243  \n[https://doi.org10.1093/wber/lhab014](https://doi.org10.1093/wber/lhab014)  \nArticle  \nDecomposing Learning Inequalities in East Africa: How Much Does Sorting Matter?  \nPaul Anand, Jere R. Behrman, Hai-Anh H. Dang, and Sam Jones  \nAbstract  \nInequalities in learning opportunities arise from both household- and school-related factors. Although these factors are unlikely to be independent, few studies have considered the extent to which sorting between schools and households might aggravate educational inequalities. To fill this gap, this article presents a novel variance decomposition, which is then applied to data from over one million children from East Africa. Results indicate that sorting accounts for around 8 percent of the test-score variance, similar in magnitude to the contribution of differences in school quality. Empirical simulations of steady-state educational inequalities reveal that policies to mitigate sorting could substantially reduce educational inequalities over the long run.  \nJEL classification: I21, I24, O15  \nKeywords: Africa, education, inequality of opportunity, sorting, variance decomposition  \n1. Introduction  \nChildren in low-and middle-income countries face a wide range of educational and developmental challenges (Pritchett 2013; Black et al. 2017; Lu et al. 2020) . Even so, average national conditions often mask considerable within-country variation, evidenced by large gaps in educational achievement along dimensions typically associated with household income inequalities (e.g., Watkins 2012; Behrman 2020) . Inequalities associated with educational achievement feed into subsequent labor market inequalities, as well as longer-term differences in social mobility. And while the presence of such educational inequalities has motivated increasing calls for investments in public services, espoused by the UN’s Sustainable  \nPaul Anand is Research Associate at the Department of Social Policy and Intervention, Oxford University, Oxford, UK and Professor at the Open University, Milton Keynes, UK; his email address is [paul.anand@open.ac.uk. Jere R. Behrman](paul.anand@open.ac.uk. Jere R. Behrman) is the William R. Kenan, Jr. Professor of Economics at the University of Pennsylvania, Philadelphia, USA; his email address is [jbehrman@sas.upenn.edu. Hai-Anh H. Dang is a senior](jbehrman@sas.upenn.edu. Hai-Anh H. Dang is a senior) economist in the Data Production and Methods Unit, Development Data Group, World Bank, Washington DC, USA; he is also affiliated with the International School, Vietnam National University, Hanoi, the Center for Analysis and Forecasting, Vietnam’s Academy of Social Sciences, GLO, Indiana University, USA, and Institut zur Zukunft der Arbeit (IZA); his email address [is hdang@worldbank.org. Sam Jones](is hdang@worldbank.org. Sam Jones) (corresponding author) is a Research Fellow with UNU-WIDER in Maputo, Mozambique; his email address is [jones@wider.unu.edu. The](jones@wider.unu.edu. The) authors thank the editor, referees, Jed Friedman, Ha Nguyen, Ron Smith, and participants at the Centre for the Study of African Economies 2019 Conference for helpful comments on earlier versions. We are grateful to the UK Department of International Development for funding assistance through its Strategic Research Program (SRP) program. The study was also prepared within the UNU-WIDER project on “Social mobility in the Global South – concepts, measures, and determinants.” Previous versions were published as World Bank Policy Research Paper \\#8622 and UNU-WIDER Working Paper 2019/110. A supplementary online appendix is available with this article at The World Bank Economic Review website.  \n© UNU-WIDER, 2021 . Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. 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