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Chinese Gini Coefficient from 2005 to 2012, Based on 20 Grouped Income Data Sets of Urban and Rural Residents

Author

Listed:
  • Jiandong Chen
  • Fuqian Fang
  • Wenxuan Hou
  • Fengying Li
  • Ming Pu
  • Malin Song

Abstract

Data insufficiency has become the primary factor affecting research on income disparity in China. To resolve this issue, this paper explores Chinese income distribution and income inequality using distribution functions. First, it examines 20 sets of grouped data on family income between 2005 and 2012 by the China Yearbook of Household Surveys, 2013, and compares the fitting effects of eight distribution functions. The results show that the generalized beta distribution of the second kind has a high fitting to the income distribution of urban and rural residents in China. Next, these results are used to calculate the Chinese Gini ratio, which is then compared with the findings of relevant studies. Finally, this paper discusses the influence of urbanization on income inequality in China and suggests that accelerating urbanization can play an important role in narrowing the income gap of Chinese residents.

Suggested Citation

  • Jiandong Chen & Fuqian Fang & Wenxuan Hou & Fengying Li & Ming Pu & Malin Song, 2015. "Chinese Gini Coefficient from 2005 to 2012, Based on 20 Grouped Income Data Sets of Urban and Rural Residents," Journal of Applied Mathematics, John Wiley & Sons, vol. 2015(1).
  • Handle: RePEc:wly:jnljam:v:2015:y:2015:i:1:n:939020
    DOI: 10.1155/2015/939020
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    References listed on IDEAS

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    1. Deaton, Angus, 1995. "Data and econometric tools for development analysis," Handbook of Development Economics, in: Hollis Chenery & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 3, chapter 33, pages 1785-1882, Elsevier.
    2. Lambert, Peter J & Aronson, J Richard, 1993. "Inequality Decomposition Analysis and the Gini Coefficient Revisited," Economic Journal, Royal Economic Society, vol. 103(420), pages 1221-1227, September.
    3. Khan, Azizur Rahman & Riskin, Carl, 2001. "Inequality and Poverty in China in the Age of Globalization," OUP Catalogue, Oxford University Press, number 9780195136494.
    4. Peter J. Lambert & Andre' Decoster, 2005. "The Gini coefficient reveals more," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 373-400.
    5. Fang, Cheng & Zhang, Xiaobo & Fan, Shenggen, 2002. "Emergence of urban poverty and inequality in China: evidence from household survey," China Economic Review, Elsevier, vol. 13(4), pages 430-443, December.
    6. Shorrocks, Anthony F, 1984. "Inequality Decomposition by Population Subgroups," Econometrica, Econometric Society, vol. 52(6), pages 1369-1385, November.
    7. Mookherjee, Dilip & Shorrocks, Anthony F, 1982. "A Decomposition Analysis of the Trend in UK Income Inequality," Economic Journal, Royal Economic Society, vol. 92(368), pages 886-902, December.
    8. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 69(2), pages 427-428, October.
    9. McDonald, James B & Mantrala, Anand, 1995. "The Distribution of Personal Income: Revisited," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 201-204, April-Jun.
    10. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
    11. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    12. Chi, Wei & Li, Bo & Yu, Qiumei, 2011. "Decomposition of the increase in earnings inequality in urban China: A distributional approach," China Economic Review, Elsevier, vol. 22(3), pages 299-312, September.
    13. Chotikapanich, Duangkamon & Griffiths, William E. & Rao, D. S. Prasada, 2007. "Estimating and Combining National Income Distributions Using Limited Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 97-109, January.
    14. Gibson, John & Huang, Jikun & Rozelle, Scott, 2001. "Why is income inequality so low in China compared to other countries?: The effect of household survey methods," Economics Letters, Elsevier, vol. 71(3), pages 329-333, June.
    15. 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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