Evidence map›Paper›PMID 34720335›Full record

ArticleSocial indicators research2022

Estimating the Impact of Covid-19 and Policy Responses on Australian Income Distribution Using Incomplete Data.

Jinjing Li, Yogi Vidyattama, Hai Anh La, Riyana Miranti, Denisa M Sologon

Abstract read
In one paragraph

Article in Social indicators research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  4. The Impacts of COVID-19 Shock on Intergenerational Income Mobility: Evidence from China.International journal of environmental research and public health · 2022
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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

5 authors.

Jinjing LiNATSEM, University of Canberra, Canberra, Australia.ORCID 0000-0001-5640-2058
Yogi VidyattamaNATSEM, University of Canberra, Canberra, Australia.
Hai Anh LaNATSEM, University of Canberra, Canberra, Australia.
Riyana MirantiNATSEM, University of Canberra, Canberra, Australia.
Denisa M SologonLuxembourg Institute of Socio-Economic Research, Luxembourg, Luxembourg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper undertakes a near real-time analysis of the income distribution effects of the Covid-19 crisis in Australia to understand the ongoing changes in the income distribution as well as the impact of policy responses. By semi-parametrically combining incomplete observed data from three different sources-the monthly Longitudinal Labour Force Survey, the Survey of Income and Housing and administrative payroll data-we estimate the impact of Covid-19 on the Australian income distribution and decompose its impact into the income shock effect and the policy effect between February and June 2020, covering the immediate periods before and after the initial Covid-19 outbreak. Our results suggest that, despite growth in unemployment, the Gini coefficient of equivalised household disposable income dropped by more than 0.02 points between February and June 2020. This reduction is due to the additional wage subsidies and welfare supports offered as part of the policy response, offsetting the increase in income inequality from the income shock effect. The results shows the effectiveness of temporary policy measures both in maintaining living standards and avoiding increases in income inequality. However, the heavy reliance on the support measures shown in the modelling raises the possibility that the changes in the income distribution may be reversed, or even that inequality and living standards could substantially worsen once the measures are withdrawn.

Indexed as

AustraliaCovid-19Income inequalityNowcasting

Identifiers

PMID34720335
PMCPMC8546393

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Registered trials

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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.