AI Article Synopsis
- Researchers often use income inequality measures designed for precise numeric data, which can lead to arbitrary rankings when applied to categorical data like self-reported health status.
- The paper introduces a new method that ensures these rankings remain consistent regardless of the numerical scale used, building on the work of Allison and Foster (2004).
- The authors demonstrate their approach with an example from the 2012 National Institute of Health Survey, expanding on rank-dependent indices to address socioeconomic health inequality effectively.
Article Abstract
When assessing socioeconomic health inequalities, researchers often draw upon measures of income inequality that were developed for ratio scale variables. As a result, the use of categorical data (such as self-reported health status) produces rankings that may be arbitrary and contingent to the numerical scale adopted. In this paper, we develop a method that overcomes this issue by providing conditions for which these rankings are invariant to the numerical scale chosen by the researcher. In doing so, we draw on the insight provided by Allison and Foster (2004) and extend their method to the dimension of socioeconomic inequality by exploiting the properties of rank-dependent indices such as Wagstaff (2002) achievement and extended concentration indices. We also provide an empirical illustration using the National Institute of Health Survey 2012.
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| http://dx.doi.org/10.1002/hec.3499 | DOI Listing |
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