الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 117 |  |  |
| | | from | 117 | | |
Abstract
To understand what life is like in a country, it is not enough to know its per capita income or the percentage of poor people, because quality of life in a country also depends on how income is distributed. Therefore, there has been an increasing awareness of knowing and measuring the size of poverty, inequality and social welfare in a country. As a result, this information has important ramification to justify or evaluate economic performance and policies. Methods to measure income inequality have for quite some time now been an important subject in statistics and econometric research. Various measures were proposed and studied. Butler-McDonald (1989) and Ahmad (1998) are among the more recent and practical ones. These measures are based on incomplete moments or incomplete conditional moments respectively, and they take into consideration the shape of the income distribution but suffer sometimes from low efficiency and or lack of robustness. On the other hand, in recent years a new inferential method called “the probability weighted moments” (PWM) was introduced and studied as a competitor to more traditional inferential methods such as the method of moments or the maximum likelihood method. In this investigation, a class of generalized measures of income inequalities using the PWM are introduced and studied. These new measures are shown to include many previously discussed ones such as the Butler-McDonald-Ahmad measures as well as others. We study these new measures under Pareto distribution. Comparison with other measures demonstrates the advantages of the new measures to previously known ones. The new measures are also shown to characterize the income distributions well. A real data application is given to illustrate the benefits of the proposed measures and their advantages. Furthermore, interdistributional income inequality measures are proposed and estimated. In addition, we study all these measures under Pareto distribution. A real data application is given to illustrate the benefits of the proposed measures and their advantages.