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Abstract-This paper offers a critical appraisal of the various methods employed to date to measure inequalities in health. It suggests that only two of these--the slope index of inequality and the concentration index-are likely to present an accurate picture of socioeconomic inequalities in health. The paper also presents several empirical examples lo illustrate of the dangers of using other measures such as the range, the Lorenz curve and the index of dissimilarity.

This paper presents and compares two threshold approaches to measuring the fairness of health care payments, one requiring that payments do not exceed a pre-specified proportion of pre-payment income, the other that they do not drive households into poverty. We develop indices for 'catastrophe' that capture the intensity of catastrophe as well as its incidence and also allow the analyst to capture the degree to which catastrophic payments occur disproportionately among poor households. Measures of poverty impact capturing both intensity and incidence are also developed. The arguments and methods are empirically illustrated with data on out-of-pocket payments from Vietnam in 1993 and 1998. This is not an uninteresting application given that 80% of health spending in that country was paid out-of-pocket in 1998. We find that the incidence and intensity of 'catastrophic' payments - both in terms of pre-payment income as well as ability to pay - were reduced between 1993 and 1998, and that both incidence and intensity of 'catastrophe' became less concentrated among the poor. We also find that the incidence and intensity of the poverty impact of out-of-pocket payments diminished over the period in question. Finally, we find that the poverty impact of out-of-pocket payments is primarily due to poor people becoming even poorer rather than the non-poor being made poor, and that it was not expenses associated with inpatient care that increased poverty but rather non-hospital expenditures.

This paper clarifies the relationship between two widely used indices of health inequality and explains why these are superior to others indices used in the literature. It also develops asymptotic estimators for their variances and clarifies the role that demographic standardization plays in the analysis of socioeconomic inequalities in health. Empirical illustrations are presented for Dutch health survey data.

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When the health sector variable whose inequality is being investigated is binary, the minimum and maximum possible values of the concentration index are equal to micro-1 and 1-micro, respectively, where micro is the mean of the variable in question. Thus as the mean increases, the range of the possible values of the concentration index shrinks, tending to zero as the mean tends to one and the concentration index tends to zero. Examples are presented on levels of and inequalities in immunization across 41 developing countries, and on changes in coverage and inequalities in selected countries.

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