Measuring Trends in U.S. Income Inequality

Theory and Applications

Häftad, Engelska, 1998

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This book presents methods for analyzing trends in income inequality over time. The different innovative techniques which the authors have developed for the examination of changes in the size distribution of income, are for the first time described in a consistent and coherent fashion. The methods are based on the maximum entropy principle which is intuitively explained in the book. New ways to analyze Lorenz curves are introduced as well as new techniques for comparing income distributions (and the level of inequality inherent herein) both over time and across space. The book should be of interest to students, scholars and policy makers alike.

Produktinformation

Utgivningsdatum1998-05-20
Mått155 x 235 x 12 mm
Vikt324 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Economics and Mathematical Systems
Antal sidor196
Upplaga1998
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540642299

Tillhör följande kategorier

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 LegendreFunctions.- 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.