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Item non-response occurs when respondents fail to provide answers to some or all of the questions posed during survey interviews. The standard procedure is to exclude such responses from the econometric analysis. This may be appropriate if the sample included does not differ significantly from those excluded in the analysis. If this is not the case, the econometric analyst faces a sample selection bias problem. The aim of this paper is to provide further evidence using a simple sequential procedure to deal with the problem when using non-randomly selected samples in social science research. The procedure entails different levels of estimation and diagnostic with the Ordinary Least Squares (OLS), Heckman's 2-step and Full Information Maximum Likelihood (FIML) estimators. In the application context, we found the FIML estimator to be more efficient in dealing with sample selection bias than the Heckman's 2-step approach.

This study applies a new method of decomposing total redistributive effect of taxation proposed by Duclos et al. (2003) to assess the redistributive effects of direct healthcare financing in Nigeria. This new framework makes it possible not only to introduce into the conventional Gini Index estimation framework a flexible ethical measure of aversion to inequality but also a novel concepts of horizontal inequity and re-ranking. The empirical results indicate that when the decision to utilize healthcare is always linked to the decision to pay for healthcare, as is the case in Nigeria, out-of-pocket payment, contrary to existing literature, may indeed be progressive with high levels of horizontal inequity and re-ranking effect. But the progressivity may underlie the lack of ability to pay by poorer households. All the components of the redistributive effect are also likely to vary with the level of the social aversion to inequity