IDEAS home Printed from https://ideas.repec.org/a/bla/apacel/v37y2023i1p39-66.html
   My bibliography  Save this article

Identifying income heterogeneity determinants using the method of moments quantile regression

Author

Listed:
  • Taiwon Ha

Abstract

Prior studies have typically concentrated on poverty status to determine anti‐poverty measures; however, this approach cannot sufficiently detect income heterogeneity. This study employs quantile regression for panel data to investigate the Korean Labour and Income Panel Study 2003–2020. Moreover, it adopts both household‐ and community‐level variables and separates demographic groups as working‐age and older adults, considering Korea's severe old‐age poverty. The findings indicate that household‐level characteristics, such as householder's gender, physical health, and employment status, present heterogeneous effects across the income distribution. Second, low‐income households are more vulnerable to regional economic and labour market downturns than high‐income neighbours. Lastly, although the National Pension, a backbone of the public pension system, provides limited supports for retirees because it was introduced much later than other countries, it assists low‐income old adults more effectively. Therefore, this study suggests more tailored redistribution measures, considering heterogeneous effects of household‐ and community‐level environments, and a further expansion of the National Pension to mitigate old‐age poverty.

Suggested Citation

  • Taiwon Ha, 2023. "Identifying income heterogeneity determinants using the method of moments quantile regression," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 37(1), pages 39-66, May.
  • Handle: RePEc:bla:apacel:v:37:y:2023:i:1:p:39-66
    DOI: 10.1111/apel.12380
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/apel.12380
    Download Restriction: no

    File URL: https://libkey.io/10.1111/apel.12380?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
    ---><---

    References listed on IDEAS

    as
    1. Koengkan, Matheus & Fuinhas, José Alberto & Kazemzadeh, Emad & Alavijeh, Nooshin Karimi & de Araujo, Saulo Jardim, 2022. "The impact of renewable energy policies on deaths from outdoor and indoor air pollution: Empirical evidence from Latin American and Caribbean countries," Energy, Elsevier, vol. 245(C).
    2. Randall S. Jones & Satoshi Urasawa, 2014. "Reducing the High Rate of Poverty Among the Elderly in Korea," OECD Economics Department Working Papers 1163, OECD Publishing.
    3. Haan, Peter & Kemptner, Daniel & Lüthen, Holger, 2020. "The rising longevity gap by lifetime earnings – Distributional implications for the pension system," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    4. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    5. Adams-Prassl, Abi & Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2020. "Inequality in the impact of the coronavirus shock: Evidence from real time surveys," Journal of Public Economics, Elsevier, vol. 189(C).
    6. Palomino, Juan C. & Rodríguez, Juan G. & Sebastian, Raquel, 2020. "Wage inequality and poverty effects of lockdown and social distancing in Europe," European Economic Review, Elsevier, vol. 129(C).
    7. Marchand, J. & Smeeding, T., 2016. "Poverty and Aging," Handbook of the Economics of Population Aging, in: Piggott, John & Woodland, Alan (ed.), Handbook of the Economics of Population Aging, edition 1, volume 1, chapter 0, pages 905-950, Elsevier.
      • Marchand, Joseph & Smeeding, Timothy, 2016. "Poverty and Aging," Working Papers 2016-11, University of Alberta, Department of Economics, revised 20 Nov 2016.
    8. Chae, Seyoung & Heshmati, Almas, 2017. "The Effects of Lifetime Work Experience on Incidence and Severity of Elderly Poverty in Korea," IZA Discussion Papers 10909, Institute of Labor Economics (IZA).
    9. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494.
    10. Marc F. Bellemare & Casey J. Wichman, 2020. "Elasticities and the Inverse Hyperbolic Sine Transformation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(1), pages 50-61, February.
    11. Inhoe Ku & Chang-O Kim & J Scott Brown, 2020. "Decomposition Analyses of the Trend in Poverty Among Older Adults: The Case of South Korea," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 75(3), pages 684-693.
    12. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr, March.
    13. Almas Heshmati & Esfandiar Maasoumi & Guanghua Wan, 2019. "An Analysis of the Determinants of Household Consumption Expenditure and Poverty in India," Economies, MDPI, vol. 7(4), pages 1-27, September.
    14. Chenhong Peng & Lue Fang & Julia Shu-Huah Wang & Yik Wa Law & Yi Zhang & Paul S. F. Yip, 2019. "Determinants of Poverty and Their Variation Across the Poverty Spectrum: Evidence from Hong Kong, a High-Income Society with a High Poverty Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 219-250, July.
    Full references (including those not matched with items on IDEAS)

    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. Carlos Díaz & Sebastian Fossati & Nicolás Trajtenberg, 2022. "Stay at home if you can: COVID‐19 stay‐at‐home guidelines and local crime," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1067-1113, December.
    2. Brandily, Paul & Brébion, Clément & Briole, Simon & Khoury, Laura, 2021. "A poorly understood disease? The impact of COVID-19 on the income gradient in mortality over the course of the pandemic," European Economic Review, Elsevier, vol. 140(C).
    3. 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.
    4. Stefanie Stantcheva, 2022. "Inequalities in the times of a pandemic," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 5-41.
    5. Andrew E. Clark & Conchita D’Ambrosio & Anthony Lepinteur, 2021. "The fall in income inequality during COVID-19 in four European countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(3), pages 489-507, September.
    6. A. Cetrulo & D. Guarascio & M. E. Virgillito, 2022. "Working from home and the explosion of enduring divides: income, employment and safety risks," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 345-402, July.
    7. Ainaa, Carmen & Brunetti, Irene & Mussida, Chiara & Scicchitano, Sergio, 2021. "Who lost the most? Distributive effects of COVID-19 pandemic," GLO Discussion Paper Series 829, Global Labor Organization (GLO).
    8. Atolia, Manoj & Papageorgiou, Chris & Turnovsky, Stephen J., 2021. "Re-opening after the lockdown: Long-run aggregate and distributional consequences of COVID-19," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    9. Michael Christl & Silvia De Poli & Dénes Kucsera & Hanno Lorenz, 2022. "COVID-19 and (gender) inequality in income: the impact of discretionary policy measures in Austria," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-17, December.
    10. Brzezinski, Michal, 2021. "The impact of past pandemics on economic and gender inequalities," Economics & Human Biology, Elsevier, vol. 43(C).
    11. Lukas Menkhoff & Carsten Schröder, 2022. "Risky Asset Holdings During Covid‐19 and their Distributional Impact: Evidence from Germany," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 497-517, June.
    12. Carmen Aina & Irene Brunetti & Chiara Mussida & Sergio Scicchitano, 2023. "Distributional effects of COVID-19," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 221-256, March.
    13. Fischer, Kai & Reade, J. James & Schmal, W. Benedikt, 2022. "What cannot be cured must be endured: The long-lasting effect of a COVID-19 infection on workplace productivity," Labour Economics, Elsevier, vol. 79(C).
    14. Isaure Delaporte & Julia Escobar & Werner Peña, 2021. "The distributional consequences of social distancing on poverty and labour income inequality in Latin America and the Caribbean," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1385-1443, October.
    15. Mello, Marco & Moscelli, Giuseppe, 2022. "Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1025-1052.
    16. Giorgia Menta, 2021. "Poverty in the COVID-19 Era: Real-time Data Analysis on Five European Countries," Research on Economic Inequality, in: Research on Economic Inequality: Poverty, Inequality and Shocks, volume 29, pages 209-247, Emerald Group Publishing Limited.
    17. Alejandra Rodríguez Sánchez & Anette Fasang & Susan Harkness, 2021. "Gender division of housework during the COVID-19 pandemic: Temporary shocks or durable change?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(43), pages 1297-1316.
    18. Mohammad Mazharul Islam & Mohammad Muzahidul Islam & Haitham Khoj, 2022. "Coping Mechanisms and Quality of Life of Low-Income Households during the COVID-19 Pandemic: Empirical Evidence from Bangladesh," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
    19. Carbonero, Francesco & Scicchitano, Sergio, 2021. "Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?," GLO Discussion Paper Series 765, Global Labor Organization (GLO).
    20. Inhoe Ku & Wonjin Lee & Seoyun Lee, 2021. "Declining Family Support, Changing Income Sources, and Older People Poverty: Lessons from South Korea," Population and Development Review, The Population Council, Inc., vol. 47(4), pages 965-996, December.

    More about this item

    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:bla:apacel:v:37:y:2023:i:1:p:39-66. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/14678411 .

    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.