IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v162y2022i1d10.1007_s11205-021-02826-0.html
   My bibliography  Save this article

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

Author

Listed:
  • Jinjing Li

    (University of Canberra)

  • Yogi Vidyattama

    (University of Canberra)

  • Hai Anh La

    (University of Canberra)

  • Riyana Miranti

    (University of Canberra)

  • Denisa M. Sologon

    (Luxembourg Institute of Socio-Economic Research)

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may be reversed, or even that inequality and living standards could substantially worsen once the measures are withdrawn.

Suggested Citation

  • Jinjing Li & Yogi Vidyattama & Hai Anh La & Riyana Miranti & Denisa M. Sologon, 2022. "Estimating the Impact of Covid-19 and Policy Responses on Australian Income Distribution Using Incomplete Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(1), pages 1-31, July.
    • Handle: RePEc:spr:soinre:v:162:y:2022:i:1:d:10.1007_s11205-021-02826-0
    DOI: 10.1007/s11205-021-02826-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-021-02826-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-021-02826-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    2. Olivier Bargain & Tim Callan, 2010. "Analysing the effects of tax-benefit reforms on income distribution: a decomposition approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(1), pages 1-21, March.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Jinjing Li & Hai Anh La & Denisa M. Sologon, 2021. "Policy, Demography, and Market Income Volatility: What Shaped Income Distribution and Inequality in Australia Between 2002 and 2016?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(1), pages 196-221, March.
    5. Mike Brewer & Iva Valentinova Tasseva, 2021. "Did the UK policy response to Covid-19 protect household incomes?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(3), pages 433-458, September.
    6. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
    7. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    8. Paul DE BEER, 2012. "Earnings and income inequality in the EU during the crisis," International Labour Review, International Labour Organization, vol. 151(4), pages 313-331, December.
    9. Olivier Bargain & Tim Callan & Karina Doorley & Claire Keane, 2017. "Changes in Income Distributions and the Role of Tax‐Benefit Policy During the Great Recession: An International Perspective," Fiscal Studies, Institute for Fiscal Studies, vol. 38, pages 559-585, December.
    10. Giovanni Bonaccorsi & Francesco Pierri & Matteo Cinelli & Andrea Flori & Alessandro Galeazzi & Francesco Porcelli & Ana Lucia Schmidt & Carlo Michele Valensise & Antonio Scala & Walter Quattrociocchi , 2020. "Economic and social consequences of human mobility restrictions under COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(27), pages 15530-15535, July.
    11. Jekaterina Navicke & Olga Rastrigina & Holly Sutherland, 2014. "Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 101-119, October.
    12. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    13. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    14. Doorley, Karina & Regan, Mark & Beirne, Keelan & Roantree, Barra & Tuda, Dora, 2020. "The potential costs and distributional effect of Covid-19 related unemployment in Ireland," EUROMOD Working Papers EM5/20, EUROMOD at the Institute for Social and Economic Research.
    15. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    16. Jinjing Li & Cathal O'Donoghue, 2014. "Evaluating Binary Alignment Methods in Microsimulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-15.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Glenn Abela, 2022. "Assessing the impacts of the COVID-19 wage supplement scheme: A microsimulation study," CBM Working Papers WP/06/2022, Central Bank of Malta.
    2. Shiqi Jiang & Lingli Qi & Xinyue Lin, 2022. "The Impacts of COVID-19 Shock on Intergenerational Income Mobility: Evidence from China," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    3. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).
    2. Jinjing Li & Yogi Vidyattama & Hai Anh La & Riyana Miranti & Denisa M Sologon, 2020. "The Impact of COVID-19 and Policy Responses on Australian Income Distribution and Poverty," Papers 2009.04037, arXiv.org.
    3. Denisa M. Sologon & Cathal O’Donoghue & Iryna Kyzyma & Jinjing Li & Jules Linden & Raymond Wagener, 2022. "The COVID-19 resilience of a continental welfare regime - nowcasting the distributional impact of the crisis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 777-809, December.
    4. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    5. Nerijus Černiauskas & Denisa M. Sologon & Cathal O’Donoghue & Linas Tarasonis, 2022. "Income Inequality and Redistribution in Lithuania: The Role of Policy, Labor Market, Income, and Demographics," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S1), pages 131-166, April.
    6. H. Xavier Jara & Lourdes Montesdeoca & Iva Tasseva, 2022. "The Role of Automatic Stabilizers and Emergency Tax–Benefit Policies During the COVID-19 Pandemic: Evidence from Ecuador," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(6), pages 2787-2809, December.
    7. Richard K. Crump & Stefano Eusepi & Domenico Giannone & Eric Qian & Argia M. Sbordone, 2021. "A Large Bayesian VAR of the United States Economy," Staff Reports 976, Federal Reserve Bank of New York.
    8. Vanda Almeida & Salvador Barrios & Michael Christl & Silvia Poli & Alberto Tumino & Wouter Wielen, 2021. "The impact of COVID-19 on households´ income in the EU," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(3), pages 413-431, September.
    9. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    10. Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.
    11. Alkhareif, Ryadh M. & Barnett, William A., 2020. "Nowcasting Real GDP for Saudi Arabia," MPRA Paper 104278, University Library of Munich, Germany.
    12. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    13. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    14. De Bruin, Kelly & Monaghan, Eoin & Yakut, Aykut Mert, 2020. "The environmental and economic impacts of the COVID-19 crisis on the Irish economy: An application of the I3E model," Research Series, Economic and Social Research Institute (ESRI), number RS106, June.
    15. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    16. Alifatussaadah, Ardiana & Primariesty, Anindya Diva & Soleh, Agus Mohamad & Andriansyah, Andriansyah, 2019. "Nowcasting Indonesia's GDP Growth: Are Fiscal Data Useful?," MPRA Paper 105252, University Library of Munich, Germany.
    17. Abdalla, Ahmed & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: a dynamic factor model approach," LSE Research Online Documents on Economics 108539, London School of Economics and Political Science, LSE Library.
    18. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    19. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    20. Danilo Cascaldi-Garcia & Thiago Revil T. Ferreira & Domenico Giannone & Michele Modugno, 2021. "Back to the Present: Learning about the Euro Area through a Now-casting Model," International Finance Discussion Papers 1313, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    Keywords

    Covid-19; Nowcasting; Income inequality; Australia;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:162:y:2022:i:1:d:10.1007_s11205-021-02826-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.