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Pandemic Schooling Mode and Student Test Scores: Evidence from US School Districts
Open accessAbstract
We estimate the impact of district-level schooling mode (in-person versus hybrid or virtual learning) in the 2020–2021 school year on students’ pass rates on standardized tests in grades 3–8 across 11 states. Pass rates declined from 2019 to 2021: an average of 12.8 percentage points in math and 6.8 in English language arts (ELA). Focusing on within-state, within–commuting zone variation in schooling mode, we estimate that districts with full in-person learning had significantly smaller declines (13.4 pp in math, 8.3 in ELA). The value to in-person learning was larger for districts with larger populations of Black students. (JEL H75, I12, I18, I21, I28, J15)
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Metadata
- Authors
- Jack, R., Halloran, C., Okun, J., & Oster, E.
- Affiliations
- University of Nebraska–Lincoln (US)
- Brown University (US)
- Massachusetts Institute of Technology (US)
- National Bureau of Economic Research (US)
- ORCID
- Jack, Rebecca
- Venue
- American Economic Review: Insights
- Publisher
- American Economic Association
- Published
- 2023-06-01
- Volume
- 5
- Issue
- 2
- Pages
- 173-190
- ISSN
- 2640-205X (ISSN-L)
- 2640-2068
- OpenAlex
- W4379932759
- Cited by
- 103
- References
- 24
- Citations by year
- 2026: 6 · 2025: 26 · 2024: 44 · 2023: 21
- FWCI
- 71.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.aeaweb.org
- Locations
- American Economic Review Insights (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Education › Education during COVID-19 pandemic
- Social Sciences › Education › School Choice and Performance
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
- Keywords
- COVID-19 pandemic, in-person learning, hybrid learning, virtual learning, academic performance, school districts, commuting zones, Black students, 2020-2021 academic year
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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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