Measuring Trends in U.S. Income Inequality Theory and Applications

Measuring Trends in U.S. Income Inequality Theory and Applications / by Hang K. Ryu, Daniel J. Slottje.

This book is the culmination of roughly seven years of joint research be­ tween us. We have both been interested in income inequality measurement for a considerably longer period of time. One author (Ryu) has a back­ ground in physics. While he was working on his Ph. D. in Physics at M. I. T. he bec...

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Bibliographic Details
Main Authors: Ryu, Hang K. (Author), Slottje, Daniel J. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1998.
Edition:1st ed. 1998.
Series:Lecture Notes in Economics and Mathematical Systems, 459
Springer eBook Collection.
Subjects:
Social policy.
Econometrics.
Economic policy.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1 Introduction
  • 1.1 Introduction
  • 1.2 A Brief Review of the Literature
  • 1.3 An Overview of Recent Trends in Income Inequality in the U.S.
  • 1.4 The Plan of the Book
  • 2 The Maximum Entropy Estimation Method
  • 2.1 Review of Jaynes’ (1979) Concentration Theorem
  • 2.2 Determination of a Maximum Entropy Density Function Given Known Moments
  • 2.3 Estimation of the Maximum Entropy (ME) Density Function When Moments are Unknown
  • 2.4 Estimation of the Exponential Density Function for N>2
  • 2.5 Asymptotic Properties of the Maximum Entropy Density function
  • 2.6 Maximum Entropy Estimation of Univariate Regression Functions
  • 2.7 Model Selection for Maximum Entropy Regression
  • 3 Capabilities and Earnings Inequality
  • 3.1 Introduction
  • 3.2 The Theory
  • 3.3 Empirical Results
  • 3.4 Summary and Concluding Remarks
  • Appendix 3.A: Derivation of an Earnings Distribution With Maximum Entropy Method
  • 4 Some New Functional Forms for Approximating Lorenz Curves
  • 4.1 Introduction
  • 4.2 A Flexible Lorenz Curve with Exponential Polynomials
  • 4.3 Approximation of the Empirical Lorenz Curve
  • 4.4 A Comparison of Two Alternative Derivations of the Lorenz Curves
  • 4.5 Choosing an Exponential Series Expansion Rather Than a Plain Series Expansion
  • 4.6 About Expanding the Inverse Distribution Rather Than a Lorenz Curve in a Series
  • 4.7 Orthonormal Basis Expansion for Discrete Ordered Income Observations
  • 4.8 A Flexible Lorenz Curve with Bernstein Polynomials
  • 4.9 Applications with Actual Data
  • 4.10 Summary and Concluding Remarks
  • 5 Comparing Income Distributions Using Index Space Representations
  • 5.1 Introduction
  • 5.2 The Theory
  • 5.3 Theil’s Entropy Measure
  • 5.4 Maximum Entropy Estimation of Share Functions
  • 5.5 Motivation for Decomposing the Share Function Through the Legendre Functions
  • 5.6 A Comparison of Index Space Analysis to Spectral Analysis
  • 5.7 Empirical Results
  • 5.8 Summary and Concluding Remarks
  • Appendix 5.A: A Review of the Concepts of Completeness, Orthonormality, and Basis
  • 6 Coordinate Space vs. Index Space Representations as Estimation Methods: An Application to How Macro Activity Affects the U.S. Income Distribution
  • 6.1 Introduction
  • 6.2 The Theory
  • 6.3 An Index Space Representation of the Share function
  • 6.4 A Comparison of the Index Space Representation with the Coordinate Space Representation
  • 6.5 The Impact of Macroeconomic Variables on the Share function
  • 6.6 Inequality Measures Associated with the Legendre Polynomial Expanded Share function
  • 6.7 The Empirical Results
  • 6.8 Summary and Concluding Remarks
  • 7 A New Method for Estimating limited Dependent Variables: An Analysis of Hunger
  • 7.1 Introduction
  • 7.2 Model Specification
  • 7.3 Posterior Odds Ratios to Compare Alternative Regression Hypotheses
  • 7.4 The Empirical Results
  • 7.5 Summary and Concluding Remarks
  • Author Index.

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