Abstract
The inequality analysis plays an important role since the beginning of the last century, in the economic, social and political debate. From the first pioneering paper of Gini, this subject has become more and more fascinating. The several tools proposed in the literature for evaluating the inequality belong basically to two families: on the one hand there are inequality curves which represent (also graphically) the local pattern of inequality in all segments of the considered population; on the other hand, inequality indexes (that often can be derived from a particular inequality curve) which summarize its measure in one number. Different indexes are needed to reveal different viewpoints toward inequality. In this paper, the features of the relatively new inequality I(p) curve are described. Beyond many theoretical results, also an empirical analysis based on real income data of Flint is performed.
© 2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Proceedings of Flint International Statistics Conference Kettering University, June 24–28, 2014
- Urban Planning for Change: Data and Projections in City of Flint Master Plans (1920, 1960 & 2013)
- More Equal and Poorer, or Richer but More Unequal?
- How We Can Evaluate the Inequality in Flint
- Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach
- Discrete Pareto Distributions
- Leverage Effect for Volatility with Generalized Laplace Error
Artikel in diesem Heft
- Frontmatter
- Proceedings of Flint International Statistics Conference Kettering University, June 24–28, 2014
- Urban Planning for Change: Data and Projections in City of Flint Master Plans (1920, 1960 & 2013)
- More Equal and Poorer, or Richer but More Unequal?
- How We Can Evaluate the Inequality in Flint
- Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach
- Discrete Pareto Distributions
- Leverage Effect for Volatility with Generalized Laplace Error