Decomposition of the Gini coefficient using Stata
The Gini coefficient is widely used to measure inequality in the distribution of income, consumption, and other welfare proxies. Decomposing this measure can help you understand the determinants of inequality. In this presentation, I will use income data from Mexico to illustrate a user-written command, descogini, that implements the Gini decomposition proposed by Lerman and Yitzhaki (1985, Review of Economics and Statistics 67: 151–156). Using this command, the Gini coefficient for total income can be decomposed in three terms: how important the income source is with respect to total income; how equally or unequally distributed the income source is; and how the income source and the distribution of total income are correlated. In the presentation, I will also illustrate how to obtain the impact that a marginal change in a particular income source will have on total income inequality, as well as how to obtain bootstrap standard errors.
Decomposing Inequality and Obtaining Marginal Effects
This article describes a user-written command, descogini, that decomposes the Gini coefficient by income source and allows the calculation of the impact that a marginal change in a particular income source will have on inequality. descogini can be used with bootstrap to obtain standard errors and confidence intervals.
The qualitative research interview
Background  Interviews are among the most familiar strategies for collecting qualitative data. The different qualitative interviewing strategies in common use emerged from diverse disciplinary perspectives resulting in a wide variation among interviewing approaches. Unlike the highly structured survey interviews and questionnaires used in epidemiology and most health services research, we examine less structured interview strategies in which the person interviewed is more a participant in meaning making than a conduit from which information is retrieved.