With great pleasure, the Department of Applied Statistics, Faculty of Economics and Administration, cordially invites you to the following webinar:
THE POWER-LAW DISTRIBUTION FOR THE INCOME OF POOR HOUSEHOLDS
Dr. Muhammad Aslam Mohd Safari (UKM)
15 September 2020 (Tuesday), 10.00 am
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 household income data. A robust method based on probability integral transform statistics 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 outperforms all the other models.
Muhammad Aslam Mohd Safari was born in Klang, Selangor, Malaysia in 1991. He received his Ph.D. degree in statistics from Universiti Kebangsaan Malaysia, in 2019. He is currently a researcher with Universiti Kebangsaan Malaysia. His research interests include income distribution, robust statistics, and statistical modeling. He has published a number of journal articles related to these areas.
All are welcome