Essays on the Econometrics of Inequality and Poverty Measurements

Book excerpt: This dissertation consists of four essays on the econometrics of inequality and poverty measurement. It provides a statistical analysis based on probabilistic models, finite mixture distributions and quantile regression models, all using aBayesian approach.Chapter 2 models income distribution using a mixture of lognormal densities. Using the analytical expression of inequality indices, it shows how a Rao-Blackwellised Gibbs sampler can lead to accurate inference on income inequality measurements even in small samples.Chapter 3 develops Bayesian inference for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). It models the considered distribution by a mixture of lognormal densities and then provides conditional posterior densities for the quantile regression parameters. This approach is perceived to provide better estimates in the extreme quantiles in the presence of heavy tails as well as valid small sample confidence intervalsfor the Oaxaca-Blinder decomposition.Chapter 4 provides Bayesian inference for a mixture of two Pareto distributions which is then used to approximate the upper tail of a wage distribution. This mixture model is applied to the data from the CPS ORG to analyze the recent structure of top wages in the U.S. from 1992 through 2009. Findings are largely in accordance with the explanations combining the model of superstars and the model of tournaments in hierarchical organization structures. Chapter 5 makes use of the RIF-regression to measure both changes in the return to education across quantiles and rural urban inequality decomposition in consumption expenditure in Senegal.