Evidence map›Paper›PMID 42691102›Full record

ArticlePloS one2026

The impact of state- versus county-level mask mandates on economic activity during the COVID-19 pandemic.

Nathan Seegert, Maclean Gaulin, Nathorn Chaiyakunapruk, Francisco Navarro-Sanchez, Danielle Nguyen, Mu-Jeung Yang, Richard E Nelson

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nathan SeegertDepartment of Finance, Northeastern University, Massachusetts, United States of America.
Maclean GaulinSchool of Accounting, University of Utah, Utah, United States of America.ORCID https://orcid.org/0000-0002-4292-5779
Nathorn ChaiyakunaprukDepartment of Pharmacotherapy, University of Utah, Utah, United States of America.
Francisco Navarro-SanchezDepartment of Economics, Allegheny College, Pennsylvania, United States of America.ORCID https://orcid.org/0009-0002-3891-592X
Danielle NguyenDepartment of Pharmacotherapy, University of Utah, Utah, United States of America.
Mu-Jeung YangDepartment of Economics, University of Oklahoma, Oklahoma, United States of America.
Richard E NelsonIDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, United States of America.ORCID https://orcid.org/0000-0002-4441-7460

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, policy makers used mask mandates as a tool to signal that, due to the amount of virus circulating at the time, social behaviors might be risky. Because of the geographically specific nature of disease transmission information, these signals would be more informative in smaller geographic units. The objective of this study was to estimate the impact of policy information revelation by quantifying the impact of state versus county mask mandates on economic activity.

methodsWe constructed a longitudinal dataset of US counties from April to September 2020 including the period immediately surrounding a COVID-19 mask mandate policy. In our primary analysis, we used a regression discontinuity approach with economic activity measured as county-level cell phone mobility and credit card spending.

resultsWe found that state mask mandates were associated with an increase in economic activity (coefficient = 1.045, 95% CI: 0.44,1.65) and spending per person (coefficient = $143, 95% CI: $102, $185). On the other hand, we found no statistically significant effects following county mask mandates.

conclusionUsing variation in the level of government enacting a policy, we find that mobility and spending are higher after a US state mask mandate than a county mandate, consistent with information revelation.

Indexed as

COVID-19MasksPandemicsHumansSARS-CoV-2United States

Identifiers

PMID42691102
PMCPMC13541194

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.