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Correcting the Bias in the Concentration Index when Income is Grouped

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  • Philip Clarke
  • Tom Van Ourti

Abstract

The problem introduced by grouping income data when measuring socioeconomic inequalities in health (and health care) has been highlighted in a recent study. We reexamine this issue and show there is a tendency to underestimate the concentration index at an increasing rate when lowering the number of income categories. This bias results from a form of measurement error and we propose two correction methods. Firstly, the use of instrumental variables (IV) can reduce the error within income categories. Secondly, through a simple formula for correction that is based only on the number of groups. We compare the performance of these methods using data from 15 European countries and the United States. We find that the simple correction formula reduces the impact of grouping and always outperforms the IV approach. Use of this correction can substantially improve comparisons of the concentration index both across countries and across time.

Suggested Citation

  • Philip Clarke & Tom Van Ourti, 2009. "Correcting the Bias in the Concentration Index when Income is Grouped," CEPR Discussion Papers 599, Centre for Economic Policy Research, Research School of Economics, Australian National University.
    • Handle: RePEc:auu:dpaper:599
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    File URL: https://www.cbe.anu.edu.au/researchpapers/CEPR/DP599.pdf
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    Cited by:

    1. David Madden, 2013. "The Socio-Economic Gradient Of Obesity In Ireland," The Economic and Social Review, Economic and Social Studies, vol. 44(2), pages 181-196.
    2. Clarke, Philip & Van Ourti, Tom, 2010. "Calculating the concentration index when income is grouped," Journal of Health Economics, Elsevier, vol. 29(1), pages 151-157, January.
    3. Camilla Sortsø & Jørgen Lauridsen & Martha Emneus & Anders Green & Peter Bjødstrup Jensen, 2017. "Socioeconomic inequality of diabetes patients’ health care utilization in Denmark," Health Economics Review, Springer, vol. 7(1), pages 1-22, December.

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    More about this item

    Keywords

    concentration index; errors-in-variables; instrumental variables; categorical data; first-order correction;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I19 - Health, Education, and Welfare - - Health - - - Other

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