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Distribution of individual incomes in China between 1992 and 2009

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  • Guo, Qiang
  • Gao, Li

Abstract

This paper presents comprehensive analysis of the evolution of the distribution of individual annual incomes across the majority of the population in China from 1992–2009. The cumulative distribution functions (CDFs) and probability density functions (PDFs) are presented. Overall, the CDFs follow the Gaussian function C(x)=Ae−(x−μ)22σ2 for the majority of individuals in the population, while the PDFs obey the function P(x)=B(x−μ)e−(x−μ)22σ2. The width of the PDF has widened from 1992 to 2009, suggesting the factor (x−μ) has been progressively skewing the curve to the right. This long tail representing the high income range is reminiscent of an exponential distribution curve. This indicates that a few individuals obtain extremely high incomes, leading to increasing levels of financial inequality in China.

Suggested Citation

  • Guo, Qiang & Gao, Li, 2012. "Distribution of individual incomes in China between 1992 and 2009," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5139-5145.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:21:p:5139-5145
    DOI: 10.1016/j.physa.2012.05.022
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    1. Repetowicz, Przemysław & Hutzler, Stefan & Richmond, Peter, 2005. "Dynamics of money and income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 356(2), pages 641-654.
    2. Banerjee, Anand & Yakovenko, Victor M. & Di Matteo, T., 2006. "A study of the personal income distribution in Australia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 54-59.
    3. Fujiwara, Yoshi & Souma, Wataru & Aoyama, Hideaki & Kaizoji, Taisei & Aoki, Masanao, 2003. "Growth and fluctuations of personal income," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(3), pages 598-604.
    4. Sinha, Sitabhra, 2006. "Evidence for power-law tail of the wealth distribution in India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 555-562.
    5. Drăgulescu, Adrian & Yakovenko, Victor M., 2001. "Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 213-221.
    6. Aoyama, Hideaki & Souma, Wataru & Fujiwara, Yoshi, 2003. "Growth and fluctuations of personal and company's income," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 352-358.
    7. Clementi, F. & Gallegati, M., 2005. "Power law tails in the Italian personal income distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 427-438.
    8. N. J. Moura & M. B. Ribeiro, 2009. "Evidence for the Gompertz curve in the income distribution of Brazil 1978–2005," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(1), pages 101-120, January.
    9. Clementi, F. & Di Matteo, T. & Gallegati, M., 2006. "The power-law tail exponent of income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 49-53.
    10. Claudio Quintano & Antonella D'Agostino, 2006. "Studying Inequality In Income Distribution Of Single‐Person Households In Four Developed Countries," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 52(4), pages 525-546, December.
    11. Hideaki Aoyama & Yuichi Nagahara & Mitsuhiro P. Okazaki & Wataru Souma & Hideki Takayasu & Misako Takayasu, 2000. "Pareto's Law for Income of Individuals and Debt of Bankrupt Companies," Papers cond-mat/0006038, arXiv.org.
    12. Alan Harrison, 1981. "Earnings by Size: A Tale of Two Distributions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 48(4), pages 621-631.
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    Cited by:

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    2. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & AL-Dhurafi, Nasr Ahmed, 2020. "The power-law distribution for the income of poor households," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Gao, Li, 2015. "Evolution of consumption distribution and model of wealth distribution in China between 1995 and 2012," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 76-86.
    4. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    5. Asif, Muhammad & Hussain, Zawar & Asghar, Zahid & Hussain, Muhammad Irfan & Raftab, Mariya & Shah, Said Farooq & Khan, Akbar Ali, 2021. "A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).

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