Journal of Income Distribution®

New Series

The Journal of Income Distribution, Volume 22, Number 2 (June 2013) is posted electronically. Hard copy of this issue is also being distributed.

About this journal
Aims and Scope
Editorial Team
Advisory Editorial Board
Previous Issues

Read to find what is at the origin of the unequal distribution of income… Is it living standards, as affirms Jesus Perez-Mayo in “Combining the dynamics of Poverty and Deprivation”, which has escaped confirming analysis from single poverty measures? Is it specific changes in the upper half of the income distribution, as argued by Charles L. Ballard, Paul L. Menchik, and Lu Tan in “The State(s) of Inequality: Changes in Income Distribution in the US States and Census Divisions, 1976-2008”? Is it the strong and gradual effect of changes in workers’ characteristics, identified by Guillermo Alves, Matías Brum, and Mijail Yapor, in “Wage Inequality on the Rise: The Role of Workers’ Characteristics”, as responsibile for the rise in wage inequality in Uruguay? Again, is it wages, specifically the presence of a part-time wage penalty for involuntary and voluntary part-time work, and lower returns for part-time workers’ labour market characteristics, as determined by Piret Tõnurist and Dimitris Pavlopoulos in “Part-Time Wage-Gap in Germany: Evidence across the Wage Distribution”? Can more appropriate distributional research methodologies be applied to the question? In “Evidence for Multiple Labor Market Segments: An Entropic Analysis of US Earned Income, 1996-2007”, Markus P. A. Schneider argues that use of exponential components affords insights – e.g., different labor market segments react differently to changing economic environments -- much harder to come by using a single parametric distribution. Ivan L. Pitt, in his article “Power Laws and Skew Distributions: An Application to Performance Royalty Income”, illustrates that for the skewness exhibited by fewer songwriters’ earning most of royalty income, the multivariate log-skew-t maximum likelihood model provides a better fit over other distribution methods.

Volume 22, Number 2: June 2013

Table of Contents

Jesus Perez-Mayo
Charles L. Ballard, Paul L. Menchik, Lu Tan
Markus P. A. Schneider
Guillermo Alves, Matías Brum, Mijail Yapor
Piret Tõnurist, Dimitris Pavlopoulos
Ivan L. Pitt