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Large socio-economic, geographic and demographic disparities exist in exposure to school closures
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Abstract
The coronovirus disease 2019 (COVID-19) pandemic has prompted many school districts to turn to distance or at-home learning. Studies are emerging on the negative effects of distance learning on educational performance, but less is known about the socio-economic, geographic and demographic characteristics of students exposed to distance learning. We introduce a U.S. School Closure and Distance Learning Database that tracks in-person visits across more than 100,000 schools throughout 2020. The database, which we make publicly accessible and update monthly, describes year-over-year change in in-person visits to each school throughout 2020 to estimate whether the school is engaged in distance learning. Our findings reveal that school closures from September to December 2020 were more common in schools with lower third-grade math scores and higher shares of students from racial/ethnic minorities, who experience homelessness, have limited English proficiency and are eligible for free/reduced-price school lunches. The findings portend rising inequalities in learning outcomes.
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“Our results show that learning loss was particularly pronounced for students from disadvantaged homes, confirming the fears held by many that school closures would cause socioeconomic gaps to widen (51 –55).”
From Learning loss due to school closures during the COVID-19 pandemic (2021) · cites this paper as [54]
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“Evidence from mobile phone locations (Parolin and Lee 2021) indicates that 9 out of 10 schools were closed in April 2020, falling to 40 percent in September but then rising again to 56 percent in December.”
From Educational Inequality* (2022) · cites this paper as Parolin and Lee 2021
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“Parolin and Lee (2021) show that school closures in the United States in the fall of 2020 were more common for students from ethnic minorities.”
From Educational Inequality* (2022) · cites this paper as Parolin and Lee (2021)
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Learning loss due to school closures during the COVID-19 pandemic
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Metadata
- Authors
- Parolin, Z., & Lee, E. K.
- Affiliations
- Bocconi University (IT)
- Columbia University (US)
- Corresponding author
- Parolin, Zachary
- Venue
- Nature Human Behaviour
- Published
- 2021-03-18
- Volume
- 5
- Issue
- 4
- Pages
- 522-528
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC8060162
- OpenAlex
- W3138870524
- Cited by
- 217
- References
- 35
- Citations by year
- 2026: 26 · 2025: 27 · 2024: 42 · 2023: 54 · 2022: 44 · 2021: 23
- FWCI
- 71.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Human Behaviour (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Bill and Melinda Gates Foundation (017972)
- Topics
- Social Sciences › Education › Education during COVID-19 pandemic
- Social Sciences › Education › School Choice and Performance
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- school closures, distance education, COVID-19 pandemic, educational inequality, socioeconomic disparities, racial and ethnic disparities, homelessness, limited English proficiency, learning outcomes
- MeSH terms
- Academic Performance, Limited English Proficiency, COVID-19, SARS-CoV-2, Adolescent, Child, Communicable Disease Control, Ethnicity, Ill-Housed Persons, Humans, Income, Minority Groups, Public Policy, Schools, Socioeconomic Factors, United States, Databases, Factual, Education, Distance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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