Cited by 1,848Education during COVID-19 pandemicSchool Choice and PerformanceCOVID-19 Pandemic Impacts

The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index; 14 of the 59 sources its claims cite were checked for support.

Learning loss due to school closures during the COVID-19 pandemic

Open access
Engzell, Per, Frey, Arun, Verhagen, Mark D.·2021·Proceedings of the National Academy of Sciences

Abstract

Significance: School closures have been a common tool in the battle against COVID-19. Yet, their costs and benefits remain insufficiently known. We use a natural experiment that occurred as national examinations in The Netherlands took place before and after lockdown to evaluate the impact of school closures on students’ learning. The Netherlands is interesting as a “best-case” scenario, with a short lockdown, equitable school funding, and world-leading rates of broadband access. Despite favorable conditions, we find that students made little or no progress while learning from home. Learning loss was most pronounced among students from disadvantaged homes.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Using Dutch national assessments taken before and after an 8-week COVID-19 school closure, this study finds that primary students made little or no progress while learning from home. The Netherlands is a “best-case” setting, yet average learning loss was 3.16 percentile points (0.08 SD), with larger losses among students from disadvantaged homes.

Key findings

  • In the baseline difference-in-differences estimate for a composite of math, spelling, and reading, students lost on average 3.16 percentile points, equivalent to 0.08 SD; raw differences ranged from −0.76 percentiles in spelling to −2.15 percentiles in math.
  • Losses were concentrated among students from less-educated homes: the two lowest parental-education categories, together 8% of the population, suffered losses 40% larger than average (high, −3.07; low, −4.34; lowest, −4.25); within-family estimates showed a gap of 60% (high, −3.25; low, −4.67; lowest, −5.20).
  • There was little evidence that effects differed by sex, school grade, subject, or prior performance, but school-level variation was considerable, with some schools seeing losses of 10 percentile points or more and others no losses or small gains.
  • On a learning-readiness test not tied to curricular content, effects shrank by nearly two-thirds (main effect −1.19 vs. −3.16), suggesting most of the drop reflects knowledge learned; 3.16 percentiles translates to 7.9 to 10.5 weeks of lost learning depending on the benchmark.

Methods

  • Natural experiment/difference-in-differences design using biannual national standardized tests in The Netherlands from 2017 to 2020, covering 15% of Dutch primary schools (n ≈ 350,000) and students aged 8 to 11 years (grades 4 to 7); 2020 tests occurred just before and after 8 weeks of nationwide closures starting March 16.
  • Main outcomes were math, spelling, reading, and a composite score, plus an oral reading fluency/learning-readiness test; scores were transformed to percentiles, with baseline models adjusting for a linear year trend and days between tests and clustering standard errors at the school level.
  • Robustness included placebo analyses, year exclusions, loss-to-follow-up adjustments via regression, propensity-score and maximum-entropy weighting, school and family fixed effects, and a specification curve analysis of more than 2,000 alternative models.

Limitations

  • The paper states that only time will tell whether students rebound, remain stable, or fall farther behind; test scores do not capture psychosocial development or societal and parental costs; and results may underestimate the full costs of school closures.
  • The paper notes that The Netherlands is a best-case scenario due to short closures, high technological preparedness, and equitable school funding, making the estimates a likely lower bound; the short closure also gave little time to adapt, and remote learning might improve with time.
  • The text makes evident that the sample covers 15% of primary schools, loss to follow-up is addressed but only a subset of students took tests after lockdown, and the within-family fixed-effects specification reduces the sample by approximately 60% and shifts it toward larger families.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1073/pnas.2022376118

Cites

97 of 97 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1. Distribution of testing dates 2017 to 2020 and timeline of 2020 school closures. Density curves show the distribution of testing dates for national standardized assessments in 2020 and the three comparison years 2017 to 2019. Vertical lines show the beginning and end of nationwide school closures in 2020. Schools closed nationally on March 16 and reopened on May 11, after 8 wk of remote learning. Our difference-in-differences design compares learning progress between the two testing dates in 2020 to that in the 3 previous years.
Fig. 1. Distribution of testing dates 2017 to 2020 and timeline of 2020 school closures. Density curves show the distribution of testing dates for national standardized assessments in 2020 and the three comparison years 2017 to 2019. Vertical lines show the beginning and end of nationwide school closures in 2020. Schools closed nationally on March 16 and reopened on May 11, after 8 wk of remote learning. Our difference-in-differences design compares learning progress between the two testing dates in 2020 to that in the 3 previous years.CC BY · PubMed Central
Fig. 2. Difference in test scores 2017 to 2020. Density curves show the difference between students’ percentile placement between the first and the second test in each of the years 2017 to 2020. Note that this graph does not adjust for confounding due to trends, testing date, or sample composition, which we address in subsequent analyses using a variety of techniques.
Fig. 2. Difference in test scores 2017 to 2020. Density curves show the difference between students’ percentile placement between the first and the second test in each of the years 2017 to 2020. Note that this graph does not adjust for confounding due to trends, testing date, or sample composition, which we address in subsequent analyses using a variety of techniques.CC BY · PubMed Central
Fig. 3. Estimates of learning loss for the whole sample and by subgroup and test. The graph shows estimates of learning loss from a difference-in-differences specification that compares learning progress between the two testing dates in 2020 to that in the 3 previous years. Statistical controls include time elapsed between testing dates and a linear trend in year. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. One percentile point corresponds to ∼0.025 SD. Where not otherwise noted, effects refer to a composite score of math, spelling, and reading. Regression tables underlying these results can be found in SI Appendix, section 7.1.
Fig. 3. Estimates of learning loss for the whole sample and by subgroup and test. The graph shows estimates of learning loss from a difference-in-differences specification that compares learning progress between the two testing dates in 2020 to that in the 3 previous years. Statistical controls include time elapsed between testing dates and a linear trend in year. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. One percentile point corresponds to ∼0.025 SD. Where not otherwise noted, effects refer to a composite score of math, spelling, and reading. Regression tables underlying these results can be found in SI Appendix, section 7.1.CC BY · PubMed Central
Fig. 4. Robustness to specification. The graph shows estimates of learning loss for the whole sample and separately by parental education, using a variety of adjustments for loss to follow-up. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. For details, see Materials and Methods and SI Appendix, sections 4.2 and 7.4–7.8.
Fig. 4. Robustness to specification. The graph shows estimates of learning loss for the whole sample and separately by parental education, using a variety of adjustments for loss to follow-up. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. For details, see Materials and Methods and SI Appendix, sections 4.2 and 7.4–7.8.CC BY · PubMed Central
Fig. 5. Knowledge learned vs. transitory influences. The graph compares estimates for the composite achievement score in our main analysis (light color) with test not designed to assess curricular content (dark color). Both sets of estimates refer to our baseline specification reported in Fig. 3. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. For details, see Materials and Methods and SI Appendix, sections 3.1 and 7.8.
Fig. 5. Knowledge learned vs. transitory influences. The graph compares estimates for the composite achievement score in our main analysis (light color) with test not designed to assess curricular content (dark color). Both sets of estimates refer to our baseline specification reported in Fig. 3. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. For details, see Materials and Methods and SI Appendix, sections 3.1 and 7.8.CC BY · PubMed Central

Verification

Claims the paper makes

23 Own result · 16 Own method · 12 Own interpretation · 10 No full text of the sources · 9 Sources not found · 4 Partly supported · 4 Not checked yet · 3 No basis given · 2 Supported so far · accuracy not yet measured

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Show all 37 claims

  • Probably not a substantial claim. 19
  • It may state two separate claims as one. 30
  • The quote only partly states this claim. 1
46 of 46
  • The paper relies on this without support.

    Students across The Netherlands take the same examination within a given year.

    “Students across The Netherlands take the same examination within a given year.”
  • The paper relies on this without support.

    The norm for transforming test results to percentile scores is the same across years, so absolute changes in performance over time are preserved.

    “Test results are transformed to percentile scores, but the norm for transformation is the same across years so absolute changes in performance over time are preserved.”
  • The paper relies on this without support.

    The oral reading fluency assessment does not test for the retention of curricular content.

    “As this part of the assessment does not test for the retention of curricular content, we would expect it to be less affected by school closures, which is indeed what we find.”
  • The authors state this as an assumption.

    Percentiles were drawn from an underlying normal distribution.

    “Assuming that percentiles were drawn from an underlying normal distribution, we use the following formula to convert between one and the other: d=Φ−10.50+δ100, [3] where δ is the treatment effect on the percentile scale, and Φ−1 is the inverse cumulative standard normal distribution.”
  • The paper relies on this without support.

    In the absence of the pandemic, the rate of progress in 2020 would have followed the same trend as in prior years.

    “We analyze the rate of progress in 2020 to that in previous years using a difference-in-differences design.”
  • The paper relies on this without support.

    Comprehension of the words is not needed and students and parents are discouraged to prepare for the oral reading fluency test.

    “Crucially, comprehension of the words is not needed and students and parents are discouraged to prepare for it.”
  • The paper relies on this without support.

    The test producer's translation keys are based on smaller samples than the study's sample.

    “However, as these keys are actually based on smaller samples than that at our disposal, we further impose a uniform distribution in our sample within cells defined by subject, grade, and testing occasion: midyear vs. end of year.”
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Metadata

Authors
Engzell, P., Frey, A., & Verhagen, M. D.
Affiliations
  • Stockholm University (SE)
  • University of Oxford (GB)
Corresponding author
  • Engzell, Per
  • Frey, Arun
  • Verhagen, Mark D.
Venue
Proceedings of the National Academy of Sciences
Published
2021-04-07
Volume
118
Issue
17
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W3143968054
Cited by
1,848
References
75
Citations by year
2026: 154 · 2025: 211 · 2024: 312 · 2023: 476 · 2022: 456 · 2021: 231
FWCI
562.67
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • SocArXiv (OSF Preprints) (submittedVersion, cc-by-nc-nd)
  • SocArXiv (OSF Preprints) (submittedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
  • Leverhulme Trust
  • University of Oxford
  • Vetenskapsrådet (2016-07099)
  • Forskningsrådet om Hälsa, Arbetsliv och Välfärd (2016-07099)
  • Organisation de Coopération et de Développement Économiques
  • Economic and Social Research Council
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
learning loss, school closures, COVID-19 pandemic, lockdown, national examinations, academic performance, home background, parental education, home-based learning, educational inequality
MeSH terms
COVID-19, SARS-CoV-2, Child, Female, Humans, Learning, Male, Netherlands, Quarantine, Schools, Pandemics

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