Assessing Inequality (Quantitative Applications In The Socia

Description:
This text reviews a set of widely used summary inequality measures, and the lesser known relative distribution method. It provides the basic rationale behind each and discusses their interconnections. It also introduces model-based decomposition of inequality over time using quantile regression. This approach enables researchers to estimate two different contributions to changes in inequality between two time points. Key features include: clear statistical explanations that provide fundamental statistical basis for understanding the new modeling framework; straightforward empirical examples which reinforce statistical knowledge and ready-to-use procedures; and, multiple approaches to assessing inequality which are introduced by starting with the basic distributional property and providing connections among approaches. This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as individual researchers.

Table of Contents:
1. Introduction 2. PDFs, CDFs, Quantile Functions, and Lorenz Curves 3. Summary Inequality Measures 4. Choices of Inequality Measures 5. Relative Distribution Methods 6. Inference Issues 7. Analyzing Inequality Trends 8. An Illustrative Application: Inequality in Income and Wealth in the United States, 1991 - 2001 REFERENCES

Author Biography:
Lingxin Hao (PhD, Sociology, 1990, University of Chicago) is Professor of Sociology at the Johns Hopkins University. She was a 2002-2003 Visiting Scholar at Russell Sage Foundation and a 2007 Resident Fellow at Spencer Foundation. Her areas of specialization include the family and public policy, social inequality, immigration, quantitative methods, and advanced statistics. The focus of her research is on social inequality, emphasizing the effects of structural, institutional, and contextual forces in addition to individual and family factors. Her research tests hypotheses derived from sociological and economic theories using advanced statistical methods and large national survey datasets. Her articles have appeared in various journals including Sociological Methodology, Sociological Methods and Research, Quality and Quantity, American Journal of Sociology, Social Forces, Sociology of Education, Social Science Research, and International Migration Review. Daniel Q. Naiman (PhD, Mathematics, 1982, University of Illinois at Urbana-Champaign) is Professor and Chair of the Applied Mathematics and Statistics at the Johns Hopkins University. He was elected as a Fellow of the Institute of Mathematical Statistics in 1997, and was an Erskine Fellow at the University of Canterbury in 2005. Much of his mathematical research has been focused on geometric and computational methods for multiple testing. He has collaborated on papers applying statistics in a variety of areas: bioinformatics, econometrics, environmental health, genetics, hydrology, and microbiology. His articles have appeared in various journals including Annals of Statistics, Bioinformatics, Biometrika, Human Heredity, Journal of Multivariate Analysis, Journal of the American Statistical Association, and Science.