Evidence map›Paper›PMID 35413528›Full record

ArticleSocial science & medicine (1982)2022

Death takes no bribes: Impact of perceived corruption on the effectiveness of non-pharmaceutical interventions at combating COVID-19.

Vincenzo Alfano, Salvatore Capasso, Salvatore Ercolano, Rajeev K Goel

Open access · greenAbstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
8.7field-weighted citation impact, top 3% of its field
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

3 citing papers in PubMed, 22 citations in OpenAlex.

  1. Fear itself.Socio-economic planning sciences · 2023
    Article
  2. Article
  3. Article
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 at 4 institutions in 3 countries.

Vincenzo AlfanoDepartment of Economics, University of Messina, Italy; Center for Economic Studies - CES-ifo, Germany. Electronic address: vincenzo.alfano@unina.it.
Salvatore CapassoInstitute for Mediterranean Studies - ISMed, Italy; Department of Economic and Business Studies, University of Napoli Parthenope, Italy.
Salvatore ErcolanoDepartment of Mathematics, Computer Science and Economics, University of Basilicata, Italy.
Rajeev K GoelDepartment of Economics, Illinois State University, United States; Innovation and International Competition, Kiel Institute for the World Economy, Germany.
Kiel Institute for the World Economy · DEParthenope University of Naples · ITUniversity of Basilicata · ITUniversity of Messina · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Corruption is considered in the literature as an activity with several externalities and spillover effects. Adding to the recent research on the corruption-COVID-19 nexus, we study the impact of corruption on coronavirus cases. High perceived levels of corruption have been proven to lead to lower institutional trust, and hence possibly to lower levels of citizen compliance with non-pharmaceutical interventions (NPIs), such as lockdowns, imposed by the authorities during the first wave of the pandemic to reduce the spread of coronavirus. Applying quantitative analysis with the use of hybrid models, we find that in countries with higher levels of perceived corruption, across alternative corruption measures, more COVID-19 cases are observed, ceteris paribus. This suggests that corruption has a detrimental effect on the spread of COVID-19, and that countries experiencing higher levels of corruption should pay extra attention when implementing NPIs.

Indexed as

COVID-19Communicable Disease ControlHumansPandemicsSARS-CoV-2CoronavirusCorruptionCOVID-19Non-pharmaceutical interventionsPandemicStringency measures

Identifiers

PMID35413528
PMCPMC8985406
OpenAlexW4223482873

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.