Evidence map›Paper›PMID 36531911›Full record

ArticleJournal of economic behavior & organization2023

Anti-social behaviour and economic decision-making: Panel experimental evidence in the wake of COVID-19.

Paul M Lohmann, Elisabeth Gsottbauer, Jing You, Andreas Kontoleon

Abstract read
In one paragraph

Article in Journal of economic behavior & organization, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 citing papers in PubMed.

  1. Article
  2. Risk, time preferences, trustworthiness and COVID-19 preventive behavior: evidence from France.The European journal of health economics : HEPAC : health economics in prevention and care · 2024
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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

4 authors.

Paul M LohmannEl-Erian Institute of Behavioural Economics and Policy, Judge Business School, University of Cambridge, United Kingdom.
Elisabeth GsottbauerInstitute of Public Finance, University of Innsbruck, Austria.
Jing YouSchool of Agricultural Economics and Rural Development, Renmin University of China, China.
Andreas KontoleonCentre for Environment, Energy and Natural Resource Governance, Department of Land Economy, University of Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We systematically examine the acute impact of exposure to a public health crisis on anti-social behaviour and economic decision-making using unique experimental panel data from China, collected just before the outbreak of COVID-19 and immediately after the first wave was overcome. Exploiting plausibly exogenous geographical variation in virus exposure coupled with a dataset of longitudinal experiments, we show that participants who were more intensely exposed to the virus outbreak became more anti-social than those with lower exposure, while other aspects of economic and social preferences remain largely stable. The finding is robust to multiple hypothesis testing and a similar, yet less pronounced pattern emerges when using alternative measures of virus exposure, reflecting societal concern and sentiment, constructed using social media data. The anti-social response is particularly pronounced for individuals who experienced an increase in depression or negative affect, which highlights the important role of psychological health as a potential mechanism through which the virus outbreak affected behaviour.

Indexed as

Anti-social behaviourCoronavirusNatural experimentPanel dataRisk preferencesSocial media dataTime preferences

Identifiers

PMID36531911
PMCPMC9744689

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.