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Cited by 389

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

The Long-Run Economic Consequences of High-Stakes Examinations: Evidence from Transitory Variation in Pollution

Open access
Ebenstein, Avraham, Lavy, Victor, Roth, Sefi·2016·American Economic Journal: Applied Economics

Abstract

Cognitive performance during high-stakes exams can be affected by random disturbances that, even if transitory, may have permanent consequences. We evaluate this hypothesis among Israeli students who took a series of matriculation exams between 2000 and 2002. Exploiting variation across the same student taking multiple exams, we find that transitory PM 2.5 exposure is associated with a significant decline in student performance. We then examine these students in 2010 and find that PM 2.5 exposure during exams is negatively associated with postsecondary educational attainment and earnings. The results highlight how reliance on noisy signals of student quality can lead to allocative inefficiency. (JEL I21, I23, I26, J24, J31, Q51, Q53)

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Metadata

Authors
Ebenstein, A., Lavy, V., & Roth, S.
Venue
American Economic Journal: Applied Economics
Published
2016-10-01
Volume
8
Issue
4
Pages
36-65
ISSN
  • 1945-7782 (ISSN-L)
  • 1945-7790
OpenAlex
W2299821297
Cited by
389
References
33
FWCI
95.84
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The Long-Run Economic Consequences of High-Stakes Examinations: Evidence from Transitory Variation in Pollution. https://app.cochat.ai/papers/10.1257/app.20150213
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Ebenstein, A., Lavy, V., & Roth, S. (2016). The Long-Run Economic Consequences of High-Stakes Examinations: Evidence from Transitory Variation in Pollution. American Economic Journal: Applied Economics. https://doi.org/10.1257/app.20150213

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.