The problem introduced by grouping income data when measuring socioeconomic inequalities in health (and health care) has been highlighted in a recent study in this journal. We re-examine 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 tendency arises due to 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 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.

Categorical data, Concentration index, Errors-in-variables, First-order correction, Instrumental variables, Monte Carlo method, data analysis, health care cost, health care utilization, health economics, health survey, income, mathematical computing, note
Econometric Methods: Single Equation Models; Single Variables (jel C2), Personal Income, Wealth, and Their Distributions (jel D31), Health: Other (jel I19),
Journal of Health Economics
Authors version
Erasmus MC: University Medical Center Rotterdam

Clarke, Ph, & van Ourti, T.G.M. (2010). Calculating the concentration index when income is grouped. Journal of Health Economics (Vol. 29, pp. 151–157). doi:10.1016/j.jhealeco.2009.11.011