Socioeconomic related health inequality as measured by a bivariate rank dependent index, of which the concentration index is a leading example, is well documented across a wide number of countries and measures. To decompose an inequality index is to ascertain the potential causes of this measured inequality. Current available regression based decomposition methods applicable to bivariate rank dependent indices impose stringent conditions in order for them to recover the parameters of interest. In this paper we suggest an alternative and less demanding technique based on a recentered influence function (RIF) regression. Because of the less stringent conditions this method imposes, it is more likely to yield the parameters of interest and is therefore preferred over current best practice. The RIF regression approach is also simple to estimate and interpret: the regression yields average marginal effects of covariates on the rank dependent index and interpretation resembles that of standard conditional mean analysis and has strong links to the treatment effects literature. Interpretation of the RIF regression method applied to a bivariate rank dependent index is illustrated by way of an empirical example of income related health utility inequality.
|Förlag||Department of Economics, Lund Universtiy|
|Status||Published - 2014|
|Namn||Working Paper / Department of Economics, School of Economics and Management, Lund University|