On Standard of Living and Infant Survival in Some East African Countries

To summarize the extent of infant survival in a country, three indices have been defined. The first one is the complement to 1000 of the infant mortality rate (expressed in per thousand births). The second one takes into account the inequality in infant survival rates between population subgroups. The third indicator adjusts the average infant survival rate by giving more weight to a population subgroup with a lower socio-economic status. The computation of the last two indicators requires the use of an inequality index and a concentration ratio.We used two measures of inequality, the Gini index and the Bonferroni index, as well as two concentration ratios, derived from the Gini index and related to the Bonferroni index. A short empirical illustration, based on seven East African countries, confirms the usefulness of the approach presented in this paper.

Estimates of Inequality Indices Based on Simple Random, Ranked Set, and Systematic Sampling

Gini index, Bonferroni index, and Absolute Lorenz index are some popular indices of inequality showing different features of inequality measurement. In general simple random sampling procedure is commonly used to estimate the inequality indices and their related inference. The key condition that the samples must be drawn via simple random sampling procedure though makes calculations much simpler but this assumption is often violated in practice as the data does not always yield simple random sample. Nonsimple random samples like Ranked set sampling or stratified sampling are gaining popularity for estimating these indices. The purpose of the present paper is to compare the efficiency of simple random sample estimates of inequality indices with their nonsimple random counterparts. Monte Carlo simulation technique is applied to get the results for some specific distributions.