The power-law distribution for the income of poor households

Muhammad Aslam Mohd Safari, Nurulkamal Masseran, Kamarulzaman Ibrahim and Nasr Ahmed AL-Dhurafi

Physica A: Statistical Mechanics and its Applications, 2020, vol. 557, issue C

Abstract: This study proposes a reverse Pareto model to describe the power-law behavior for the lower tail data of income distribution and illustrates an application on Malaysian and Italian household income data. A robust method based on probability integral transform statistic is used for estimating the shape parameter of the reverse Pareto model to allow for the existence of outlying observations in the lower tail data. Besides that, the optimal threshold of reverse Pareto is determined by using Kolmogorov–Smirnov statistic. It is found that the fitted reverse Pareto adequately describes the lower tail data of both datasets, suggesting that the power-law behavior is obeyed. In addition, the estimated optimal threshold of reverse Pareto model can be utilized as an alternative measure for the relative poverty line. Based on the reverse Pareto model, the Lorenz curve, Gini and Theil coefficients are determined, and it is found that low income inequality is observed for the period of the study. The fitted Lorenz curve shows that nearly 80% of the total household income is owned by the bottom 80%, whereas the remaining total household income is owned by the top 20%. Finally, comparison of the reverse Pareto model with some alternative distributions such as shifted reverse exponential, shifted reverse stretched exponential and shifted reverse lognormal in terms of model fitting for the lower tail data is also conducted. The results show that the reverse Pareto model outperform all the other models.

Keywords: Power-law; Reverse Pareto distribution; Income distribution of poor household; Probability integral transform statistic; Lower tail model selection (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437120304623
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304623

DOI: 10.1016/j.physa.2020.124893

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().