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The power-law tail exponent of income distributions
Institution:1. Department of Public Economics, University of Rome “La Sapienza”, Via del Castro Laurenziano 9, 00161 Rome, Italy;2. Applied Mathematics, Research School of Physical Sciences and Engineering, The Australian National University, 0200 Canberra, Australia;3. Department of Economics, Università Politecnica delle Marche, Piazzale Martelli 8, 60121 Ancona, Italy;1. Department of Applied Mathematics, Research School of Physical Sciences and Engineering, The Australian National University, Canberra ACT 0200, Australia;2. Department of Applied Mathematics, Research School of Physical Sciences and Engineering, The Australian National University, Canberra ACT 0200, Australia;1. Department of Corporate Finance, Faculty of Business Economics in Ko?ice, University of Economics in Bratislava, Slovakia;2. Department of Quantitative Methods, Faculty of Business Economics in Ko?ice, University of Economics in Bratislava, Slovakia;3. Department of Economics, Faculty of Business Economics in Ko?ice, University of Economics in Bratislava, Slovakia;1. Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan;2. Graduate School of Human Development and Environment, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, Japan;1. Centre for Pharmaceutical Innovation and Development (CPID), School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA 5000, Australia;2. Ian Wark Research Institute, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia;3. ANSTO, Locked Bag 2001, Kirrawee, NSW 2232, Australia;4. Nanomedicine-Laboratory of Immunology and Molecular Biomedical Research, Centre for Molecular and Medical Research, School of Medicine, Faculty of Health, Deakin University, Pigdons Road, Waurn Ponds, Geelong, Victoria 3216, Australia;1. Department of Energy Systems Research, Ajou University, Suwon 443-749, Republic of Korea;2. National Fusion Research Institute(NFRI), Gwahangno 113, Daejeon 305-333, Republic of Korea;3. Department of Physics, Ajou University, Suwon 443-749, Republic of Korea
Abstract:In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimizing the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data for Australia and Italy.
Keywords:
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