Inequality in household adaptation to schooling shocks: Covid-induced online learning engagement in real time
Open accessAbstract
We use high frequency internet search data to study in real time how US households sought out online learning resources as schools closed due to the Covid-19 pandemic. By April 2020, nationwide search intensity for both school- and parent-centered online learning resources had roughly doubled relative to pre-Covid levels. Areas of the country with higher income, better internet access and fewer rural schools saw substantially larger increases in search intensity. The pandemic will likely widen achievement gaps along these dimensions given schools' and parents' differing engagement with online resources to compensate for lost school-based learning time. Accounting for such differences and promoting more equitable access to online learning could improve the effectiveness of education policy responses to the pandemic. The public availability of internet search data allows our analyses to be updated when schools reopen and to be replicated in other countries.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Bacher et al. stated that students in rural communities will require more support to bridge the academic gap experienced with their peers who live in urban settings.
“Furthermore, there were no academic performance variations between rural and urban learners [46], this finding varied with the finding of Bacher et al. [47] who stated that students in rural communities will require more support to bridge the academic gap experienced with their peers who live in urban settings.”
From Impact of online learning on student's performance and engagement: a systematic review (2024) · cites this paper as [47]
The Discussion says students in rural communities are likely to need additional support and links improved access to preventing wider achievement gaps. The Results show lower growth in searches for learning resources in areas with more rural schools, but the paper does not establish an academic-performance gap between rural and urban students. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe results confirm the fears held by many that school closures would cause socioeconomic gaps to widen.
“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 [53]
The Introduction reports that school closures widened socioeconomic gaps in searches for online learning resources. The Discussion says this pattern suggests academic gaps will be wider in future school years, but the paper does not establish that closures had already widened learning-loss gaps; its measured outcome is primarily search engagement. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Predicting the Present with Google Trends
CHOI, H., & VARIAN, H. (2012) · Economic Record · doi:10.1111/j.1475-4932.2012.00809.x · Cited by 2,354 · see citing works
Projecting the Potential Impact of COVID-19 School Closures on Academic Achievement
Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Liu, J. (2020) · Educational Researcher · doi:10.3102/0013189x20965918 · Cited by 997 · see citing works
The impact of COVID-19 on student experiences and expectations: Evidence from a survey
Aucejo, E. M., French, J., Araya, M. P. U., & Zafar, B. (2020) · Journal of Public Economics · doi:10.1016/j.jpubeco.2020.104271 · Cited by 896 · see citing works
Using Internet Searches for Influenza Surveillance
Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 567 · see citing works
The cost of racial animus on a black candidate: Evidence using Google search data
Stephens-Davidowitz, S. I. (2014) · Journal of Public Economics · doi:10.1016/j.jpubeco.2014.04.010 · Cited by 443 · see citing works
Going to a Better School: Effects and Behavioral Responses
Pop-Eleches, C., & Urquiola, M. (2013) · American Economic Review · doi:10.1257/aer.103.4.1289 · Cited by 412 · see citing works
What Do Parents Want From Schools? Evidence From the Internet
Schneider, M., & Buckley, J. (2002) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737024002133 · Cited by 375 · see citing works
Media Influences on Social Outcomes: The Impact of MTV’s <i>16 and Pregnant</i> on Teen Childbearing
Kearney, M. S., & Levine, P. B. (2015) · American Economic Review · doi:10.1257/aer.20140012 · Cited by 316 · see citing works
Parental Effort, School Resources, and Student Achievement
Houtenville, A. J., & Conway, K. S. (2008) · The Journal of Human Resources · doi:10.3368/jhr.43.2.437 · Cited by 311 · see citing works
School Inputs, Household Substitution, and Test Scores
Das, J., Dercon, S., Habyarimana, J., Krishnan, P., Muralidharan, K., & Sundararaman, V. (2013) · American Economic Journal Applied Economics · doi:10.1257/app.5.2.29 · Cited by 246 · see citing works
Inequality in learning opportunities during Covid-19: Evidence from library takeout
Jæger, M. M., & Blaabæk, E. H. (2020) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2020.100524 · Cited by 246 · see citing works
U.S. College Students’ Internet Use: Race, Gender and Digital Divides
Jones, S., Johnson-Yale, C., Millermaier, S., & Pérez, F. S. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01439.x · Cited by 233 · see citing works
SCALING THE DIGITAL DIVIDE: HOME COMPUTER TECHNOLOGY AND STUDENT ACHIEVEMENT
Vigdor, J. L., Ladd, H. F., & MARTINEZ, E. (2014) · Economic Inquiry · doi:10.1111/ecin.12089 · Cited by 218 · see citing works
Bucy, E. P. (2000) · Harvard International Journal of Press/Politics · doi:10.1177/1081180x00005001005 · Cited by 213 · see citing works
The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey
Aucejo, E. M., French, J., Araya, M. P. U., et al. (2020) · National Bureau of Economic Research · doi:10.3386/w27392 · Cited by 189 · see citing works
Scaling the Digital Divide: Home Computer Technology and Student Achievement
Vigdor, J. L., & Ladd, H. F. (2010) · National Bureau of Economic Research · doi:10.3386/w16078 · Cited by 148 · see citing works
Hamilton, L., Grant, D. G., Kaufman, J. H., et al. (2020) · RAND Corporation eBooks · doi:10.7249/rra168-1 · Cited by 75 · see citing works
Perspectives on Internet Use: Access, Involvement and Interaction
Rice, R. E., & Haythornthwaite, C. (2010) · SAGE Publications Ltd eBooks · doi:10.4135/9781446211304.n6 · Cited by 36 · see citing works
Creating Community with Media: History, Theories and Scientific Investigations
Jankowski, N. W. (2010) · SAGE Publications Ltd eBooks · doi:10.4135/9781446211304.n4 · Cited by 30 · see citing works
Kuhfeld, M. R., Soland, J. G., Tarasawa, B., Johnson, A., Ruzek, E. A., & Liu, J. (2020) · Cited by 11 · see citing works
Aucejo, E. M., French, J., Araya, M. P. U., et al. (2020) · National Bureau of Economic Research · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Learning loss due to school closures during the COVID-19 pandemic
Engzell, P., Frey, A., & Verhagen, M. D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022376118 · Cited by 1,848 · see citing works
Learning loss due to school closures during the COVID-19 pandemic
Engzell, P., Frey, A., & Verhagen, M. D. (2020) · doi:10.31235/osf.io/ve4z7 · Cited by 1,739 · see citing works
Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic
Clark, A. E., Nong, H., Zhu, H., & Zhu, R. (2021) · China Economic Review · doi:10.1016/j.chieco.2021.101629 · Cited by 271 · see citing works
Impact of online learning on student's performance and engagement: a systematic review
Akpen, C. N., Asaolu, S., Atobatele, S., Okagbue, H., & Sampson, S. (2024) · Discover Education · doi:10.1007/s44217-024-00253-0 · Cited by 205 · see citing works
“…there were no academic performance variations between rural and urban learners [46], this finding varied with the finding of Bacher et al. [47] who stated that students in rural communities will require more support to bridge the academic gap experienced with their peers who live in urban…”
Pandemic Schooling Mode and Student Test Scores: Evidence from US States
Halloran, C., Jack, R., Okun, J., & Oster, E. (2021) · doi:10.3386/w29497 · Cited by 110 · see citing works
Experimental evidence on learning using low-tech when school is out
Angrist, N., Bergman, P., & Matsheng, M. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01381-z · Cited by 105 · see citing works
Zooming to Class? Experimental Evidence on College Students’ Online Learning during COVID-19
Kofoed, M. S., Gebhart, L., Gilmore, D., & Moschitto, R. (2024) · American Economic Review: Insights · doi:10.1257/aeri.20230077 · Cited by 125 · see citing works
The COVID-19 pandemic and its potential enduring impact on children
Irwin, M., Lazarevic, B., Soled, D., & Adesman, A. (2022) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000001097 · Cited by 72 · see citing works
Alhalafawy, W. S., & Tawfiq Zaki, M. Z. (2022) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v17i06.28885 · Cited by 69 · see citing works
Blanden, J., Doepke, M., & Stuhler, J. (2022) · National Bureau of Economic Research · doi:10.3386/w29979 · Cited by 47 · see citing works
- Authors
- Bacher-Hicks, A., Goodman, J., & Mulhern, C.
- Affiliations
- Boston University (US)
- RAND Corporation (US)
- Corresponding author
- Bacher-Hicks, Andrew
- Venue
- Journal of Public Economics
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 193
- Pages
- 104345
- ISSN
- 0047-2727 (ISSN-L)
- PMC
- PMC8486492
- OpenAlex
- W3043391138
- Cited by
- 402
- References
- 21
- Citations by year
- 2026: 28 · 2025: 48 · 2024: 62 · 2023: 70 · 2022: 97 · 2021: 89 · 2020: 8
- FWCI
- 100.55
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Public Economics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Econstor (Econstor) (submittedVersion)
- Brown Digital Repository
- Topics
- Social Sciences › Education › Education during COVID-19 pandemic
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
- Social Sciences › Education › School Choice and Performance
- Keywords
- COVID-19 pandemic, online learning, school closures, household adaptation, educational inequality, achievement gap, internet search data, internet access, rural schools, income inequality, parental engagement, education policy, real-time data
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 ›
Are you an author, or do you see a problem with this page? Request a review
