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Assessing Inequality
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Assessing Inequality



May 2010 | 160 pages | SAGE Publications, Inc
This text reviews a set of widely used summary inequality measures, and the lesser known relative distribution method 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:

- Clear statistical explanations provide fundamental statistical basis for understanding the new modeling framework

- Straightforward empirical examples reinforce statistical knowledge and ready-to-use procedures

- Multiple approaches to assessing inequality 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 behavioural sciences, as well as individual researchers.

 
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

Lingxin Hao

Lingxin Hao is a professor of sociology at Johns Hopkins University. Her specialties include quantitative methodology, social inequality, sociology of education, migration, and family and public policy. She is the lead author of two QASS monographs Quantile Regression and Assessing Inequality. Her research has appeared in the Sociological Methodology, Sociological Methods and Research, American Journal of Sociology, Demography, Social Forces, Sociology of Education, and Child Development, among others. More About Author

Daniel Q. Naiman

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... More About Author

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ISBN: 9781412926294
$ 30.00

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