IDEAS home Printed from https://ideas.repec.org/a/bla/socsci/v101y2020i2p712-731.html
   My bibliography  Save this article

Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys

Author

Listed:
  • Vladimir Hlasny

Abstract

Objective This study evaluates the bias to inequality measurement from survey nonrespondents. Methods Sixty‐six Luxembourg Income Study surveys for 38 middle‐ and high‐income countries, encompassing some 900,000 households, are used to derive estimates of the Gini coefficient for countries and selected world regions. Results Household nonresponse typically biases national Ginis downward by 1–8 percentage points. The Gini for North America appears robust to nonresponse, rising by a mere 0.34 percentage points to 44.72 when a correction is applied. The Gini for the European Union Single Market is sensitive to nonresponse, rising by 5.52 percentage points to 37.63. The OECD‐wide Gini rises by 5.93 points to 44.58. Conclusion These results appear consistent with one another and with prior evidence. They suggest that, across countries and selected world regions, household nonresponse biases the Ginis downward by 1–8 percentage points.

Suggested Citation

  • Vladimir Hlasny, 2020. "Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 712-731, March.
    • Handle: RePEc:bla:socsci:v:101:y:2020:i:2:p:712-731
    DOI: 10.1111/ssqu.12762
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/ssqu.12762
    Download Restriction: no
    File URL: https://libkey.io/10.1111/ssqu.12762?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
    ---><---

    Citations

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


    Cited by:

    1. Vladimir Hlasny, 2019. "Redistributive Impacts of Fiscal Policies in Mexico: Corrections for Top Income Measurement Problems," LIS Working papers 765, LIS Cross-National Data Center in Luxembourg.
    2. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
    3. Nishant Yonzan & Branko Milanovic & Salvatore Morelli & Janet Gornick, 2022. "Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 67-95, March.
    4. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
    5. Rafael Carranza & Marc Morgan & Brian Nolan, 2023. "Top Income Adjustments and Inequality: An Investigation of the EU‐SILC," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(3), pages 725-754, September.
    6. Ercio Muñoz & Salvatore Morelli, 2021. "kmr: A command to correct survey weights for unit nonresponse using groups’ response rates," Stata Journal, StataCorp LP, vol. 21(1), pages 206-219, March.
    7. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    8. Haiyuan Wan & Yangcheng Yu, 2023. "Correction of China's income inequality for missing top incomes," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1769-1791, August.
    9. José María Sarabia & Vanesa Jorda, 2020. "Lorenz Surfaces Based on the Sarmanov–Lee Distribution with Applications to Multidimensional Inequality in Well-Being," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    10. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," Working Papers hal-04093646, HAL.
    11. Vladimir Hlasny, 2017. "Different Faces of Inequality across Asia: Decomposition of Income Gaps across Demographic Groups," LIS Working papers 691, LIS Cross-National Data Center in Luxembourg.

    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:socsci:v:101:y:2020:i:2:p:712-731. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: http://www.blackwellpublishing.com/journal.asp?ref=0038-4941 .

    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.