Fitting parametric lorenz curves to grouped income distributions ? A critical note

1994 ◽  
Vol 19 (3) ◽  
pp. 361-370 ◽  
Author(s):  
Martin Schader ◽  
Friedrich Schmid
Keyword(s):  
Lorenz Curves ◽  
Income Distributions ◽  
Critical Note

scholarly journals Intersecting Lorenz curves and aversion to inverse downside inequality

2020 ◽  
Author(s):  
W. Henry Chiu
Keyword(s):  
Gini Coefficient ◽  
Linear Inequality ◽  
The Other ◽  
Inequality Aversion ◽  
Inequality Index ◽  
Lorenz Curves ◽  
Income Distributions ◽  
The Gini Coefficient ◽  
Precise Condition

Abstract This paper defines and characterizes the concept of an increase in inverse downside inequality and show that, when the Lorenz curves of two income distributions intersect, how the change from one distribution to the other is judged by an inequality index exhibiting inverse downside inequality aversion often depends on the relative strengths of its aversion to inverse downside inequality and inequality aversion. For the class of linear inequality indices, of which the Gini coefficient is a member, a measure characterizing the strength of an index’s aversion to inverse downside inequality against its own inequality aversion is shown to determine the ranking by the index of two distributions whose Lorenz curves cross once. The precise condition under which the same result generalizes to the case of multiple-crossing Lorenz curves is also identified.

scholarly journals Quando a Epidemiologia Encontra a Moderna Fenomenologia de Cinética Química: Efeito das Estratégias de Controle de Difusão da Pandemia Causada pela COVID-19

2020 ◽  
Vol 14 (27) ◽  
pp. 89-93
Author(s):  
Valter H. Carvalho-Silva
Keyword(s):  
New York ◽  
Infectious Diseases ◽  
Mitigation Measures ◽  
Systems Science ◽  
Rate Theory ◽  
Stat Phys ◽  
Chemical Transformations ◽  
Lorenz Curves ◽  
Income Distributions ◽  
Rate Processes

Referências 1. Keeling, M. J. & Rohani, P. Modeling infectious diseases in humans and animals. Modeling Infectious Diseases in Humans and Animals (PRINCETON UNIVERSITYPRESS, 2011). 2. Aquilanti, V., Coutinho, N. D. & Carvalho-Silva, V. H. Kinetics of Low-Temperature Transitions and Reaction Rate Theory from Non-Equilibrium Distributions. Philos. Trans. R. Soc. London A 375, 20160204 (2017). 3. Carvalho-Silva, V. H., Coutinho, N. D. & Aquilanti, V. Temperature dependence of rate processes beyond Arrhenius and Eyring: Activation and Transitivity. Front. Chem. 7, 380 (2019). 4. Center For Systems Science And Engineering Johns Hopkins University. CSSEGISandData/COVID-19 (2020). Available at: https://github.com/CSSEGISandData/COVID-19. (Accessed: 30th March 2020) 5. Machado, H. G. et al. “Transitivity”: a code for computing kinetic and related parameters in chemical transformations and transport phenomena. Molecules 24, 3478 (2019). 6. Aquilanti, V., Borges, E. P., Coutinho, N. D., Mundim, K. C. & Carvalho-Silva, V. H. From statistical thermodynamics to molecular kinetics: the change, the chance and the choice. Rend. Lincei. Sci. Fis. e Nat. 28, 787–802 (2018). 7. Arnold, B. C. Pareto and Generalized Pareto Distributions. in Modeling Income Distributions and Lorenz Curves 119–145 (Springer New York, 2008). 8. Tsallis, C. Possible Generalization of Boltzmann-Gibbs Statistics. J. Stat. Phys. 52, 479–487 (1988). 9. Jena, A. K. & Chaturvedi, M. C. Phase transformation in materials. (Prentice Hall, 1992). 10. Poccia, N. et al. Evolution and control of oxygen order in a cuprate superconductor. Nat. Mater. 10, 733–736 (2011). 11. Zhao, S., Musa, S. S., Fu, H., He, D. & Qin, J. Simple framework for real-time forecast in a data-limited situation: The Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example. Parasites and Vectors 12, 344 (2019). 12. Subbaraman, N. Coronavirus tests: researchers chase new diagnostics to fight the pandemic. Nature (2020). doi:10.1038/d41586-020-00827-6 13. Balilla, J. Assessment of COVID-19 Mass Testing: The Case of South Korea. SSRN Electron. J. (2020). doi:10.2139/ssrn.3556346 14. Anderson, R. M., Heesterbeek, H., Klinkenberg, D. & Hollingsworth, T. D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet 395, 931–934 (2020).

Characterizations of Lorenz curves and income distributions

2000 ◽  
Vol 17 (4) ◽  
pp. 639-653 ◽  
Author(s):  
Rolf Aaberge
Keyword(s):  
Lorenz Curves ◽  
Income Distributions

scholarly journals Income Inequality in Pakistan: An Analysis of Existing Evidence

1984 ◽  
Vol 23 (2-3) ◽  
pp. 365-379 ◽  
Author(s):  
Zafar Mahmood
Keyword(s):  
Standard Deviation ◽  
Income Distribution ◽  
Gini Coefficient ◽  
Coefficient Of Variation ◽  
Single Measure ◽  
Lorenz Curves ◽  
Inequality Measures ◽  
Income Distributions ◽  
Income Inequalities ◽  
Rank Distributions

To study the consequences of an economic change on income distribution we rank distributions of income at different points in time and quantify the degree of income inequalities. Changes in income distribution can be ascertained either through drawing the Lorenz curves or through estimating different inequality indices, such as Gini Coefficient, coefficient of variation, standard deviation of logs of in• comes, Theil's Index and Atkinson's Index. Ranking the distributions of income through Lorenz curves is, of course, possible only as long as they do not intersect. Moreover, when Lorenz curves do not intersect each other, all inequality measures rank income distributions uniformly. However, if the Lorenz curves do intersect each other. different inequality measures may rank income distributions differently and thus the direction of change cannot be determined unambiguously. For this reason , the use of a single measure would be misleading. Accordingly , the use of a 'package' of inequality measures becomes essential.

Modeling income distributions and Lorenz curves

2010 ◽  
Vol 8 (4) ◽  
pp. 525-526 ◽  
Author(s):  
Daniel Slottje
Keyword(s):  
Lorenz Curves ◽  
Income Distributions

Visualizing and Testing Convergence Between Two Income Distributions

2009 ◽  
Author(s):  
John A Bishop ◽  
K. Victor Chow ◽  
Lester A. Zeager
Keyword(s):  
United States ◽  
Statistical Inference ◽  
Functional Form ◽  
The United States ◽  
Underlying Distribution ◽  
Lorenz Curves ◽  
Income Distributions ◽  
Inference Tests

We use the Interdistributional Lorenz Curves (ILCs) of Butler and McDonald (1987) to visualize convergence or divergence between income distributions. To illustrate the idea, we compare income distributions from Spain, Italy, and Germany. We also offer methods to test for significant differences between the 45-degree line and an ILC, or between ILCs in different years. The tests apply to any partial moment of the distributions and impose no prior restrictions on the functional form of the underlying distribution. We illustrate the statistical inference tests by an application to income distributions for whites and non-whites in the United States.

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