Measuring Trends in U.S. Income Inequality

DANIEL SLOTTJE is a Senior Managing Director in FTI Consulting Inc.'s Forensic and Litigation Consulting practice, and provides consulting services to clients in various industries. He has significant experience in litigation consulting in intellectual property matters, including patent infringement issues, copyrights and trademarks, as well as trade secrets. In addition to advising counsel, he has provided testimony in these and other matters. Dr. Slottje is a Professor of Economics at Southern Methodist University in Dallas and is a former partner in an international consulting firm. He has published more than 120 articles and written several books on economic issues. Dr. Slottje was ranked one of the top three applied econometricians in the world (out of 5,000 people), based on number of published articles in top econometrics journals in 1999. He has been quoted in national newspapers and has appeared on Fox, CBS, and NBC affiliates.

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.