Policy, Demography, and Market Income Volatility: What Shaped Income Distribution and Inequality in Australia Between 2002 and 2016?

Li, Jinjing; La, Hai Anh; Sologon, Denisa Maria

doi:10.1111/roiw.12467

Cited by 19 publications

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Confidence intervals are smallest for the tax-benefit transformations. This is commonly found in decomposition exercises with a deterministic tax-benefit model (Li et al 2020;Paulus and Tasseva 2017). In our case, the tax-benefit simulations mainly rely on a deterministic EUROMOD tax-benefit calculator and only partly on regression-based models (for partially and non-simulated variables required in each country model).…”

Section: Gini Coefficients Of Disposable and Market Incomes And The Nmentioning

confidence: 75%

Accounting for differences in income inequality across countries: tax-benefit policy, labour market structure, returns and demographics

Sologon

Kerm

et al. 2020

J Econ Inequal

Self Cite

View full text Add to dashboard Cite

This paper presents a framework for studying international differences in the distribution of household income. Integrating micro-econometric and micro-simulation approaches in a decomposition analysis, it quantifies the role of tax-benefit systems, employment and occupational structures, labour and financial market returns, and demographic composition in accounting for differences in income inequality across countries. Building upon EUROMOD (the European tax-benefit calculator) and its harmonised datasets, the model is portable and can be implemented for crosscountry comparisons between any participating country. An application to the UK and Ireland-two countries that have much in common while displaying different levels of inequality-shows that differences in tax-benefit rules between the two countries account for over one third of the observed difference in disposable household income inequality. Demographic differences play negligible roles. The Irish tax-benefit system is more redistributive than UK's due to a higher tax progressivity and higher average transfer rates. These are largely attributable to policy parameter differences, but also to differences in pre-tax, pre-transfer income distributions.

show abstract

Section: Gini Coefficients Of Disposable and Market Incomes And The Nmentioning

confidence: 75%

Accounting for differences in income inequality across countries: tax-benefit policy, labour market structure, returns and demographics

Sologon

Kerm

et al. 2020

J Econ Inequal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared with February 2020, when the Gini was around 0.33, the Gini was hovering around 0.31 in April, May and June. A drop of 0.02 Gini points in a few months is substantial given that the annual change in disposable income inequality measured by the Gini coefficient in Australia is usually smaller than 0.01 (Li et al, 2021 ). For example, to provide a comparison, the change in the Gini coefficient between 2007 and 2009 was less than 0.012 over the two years.…”

Section: Resultsmentioning

confidence: 99%

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

Vidyattama

et al. 2021

Soc Indic Res

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…While existing research has documented a significant rise in income volatility for several countries since the 1970s (Dynan, Elmendorf et al 2012;Hardy and Ziliak 2014;Jansen 2004) there is not much evidence of year-to-year income volatility for Australia. Li, La et al (2020) explore the year-on-year changes in Australian income distribution over 15 years and identify fluctuations in market income (job losses and wage and non-wage cuts) to be the main driving change in income inequality.…”

Section: Why Was This Exploratory Project Conducted?mentioning

confidence: 99%

Using flow data to understand housing insecurity: an exploratory analysis

Yanotti¹,

Banks²,

Silva³

et al. 2021

AHURI Final Report

View full text Add to dashboard Cite

AHURIAHURI is a national independent research network with an expert not-for-profit research management company, AHURI Limited, at its centre. AHURI's mission is to deliver high quality research that influences policy development and practice change to improve the housing and urban environments of all Australians.Using high quality, independent evidence and through active, managed engagement, AHURI works to inform the policies and practices of governments and the housing and urban development industries, and stimulate debate in the broader Australian community.AHURI undertakes evidence-based policy development on a range of priority policy topics that are of interest to our audience groups, including housing and labour markets, urban growth and renewal, planning and infrastructure development, housing supply and affordability, homelessness, economic productivity, and social cohesion and wellbeing. AHURI Limited also gratefully acknowledges the contributions, both financial and in-kind, of its university research partners who have helped make the completion of this material possible. DisclaimerThe opinions in this report reflect the views of the authors and do not necessarily reflect those of AHURI Limited, its Board, its funding organisations or Inquiry Panel members. No responsibility is accepted by AHURI Limited, its Board or funders for the accuracy or omission of any statement, opinion, advice or information in this publication. AHURI journalAHURI Final Report journal series is a refereed series presenting the results of original research to a diverse readership of policy makers, researchers and practitioners. Peer review statementAn objective assessment of reports published in the AHURI journal series by carefully selected experts in the field ensures that material published is of the highest quality. The AHURI journal series employs a double-blind peer review of the full report, where anonymity is strictly observed between authors and referees.

show abstract

Policy, Demography, and Market Income Volatility: What Shaped Income Distribution and Inequality in Australia Between 2002 and 2016?

Cited by 19 publications

References 36 publications

Accounting for differences in income inequality across countries: tax-benefit policy, labour market structure, returns and demographics

Accounting for differences in income inequality across countries: tax-benefit policy, labour market structure, returns and demographics

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

Using flow data to understand housing insecurity: an exploratory analysis

Contact Info

Product

Resources

About