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Evolution of consumption distribution and model of wealth distribution in China between 1995 and 2012

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  • Gao, Li

Abstract

We study the evolution of the distribution of consumption of individuals in the majority population in China during the period 1995–2012 and find that its probability density functions (PDFs) obey the rule Pc(x)=K(x−μ)e−(x−μ)22σ2. We also find (i) that the PDFs and the individual income distribution appear to be identical, (ii) that the peaks of the PDFs of the individual consumption distribution are consistently on the low side of the PDFs of the income distribution, and (iii) that the average of the marginal propensity to consume (MPC) is large, MPC¯=0.77, indicating that in the majority population individual consumption is low and strongly dependent on income. The long right tail of the PDFs of consumption indicates that few people in China are participating in the high consumption economy, and that consumption inequality is high. After comparing the PDFs of consumption with the PDFs of income we obtain the PDFs of residual wealth during the period 1995–2012, which exhibit a Gaussian distribution. We use an agent-based kinetic wealth-exchange model (KWEM) to simulate this evolutional process and find that this Gaussian distribution indicates a strong propensity to save rather than spend. This may be due to an anticipation of such large potential outlays as housing, education, and health care in the context of an inadequate welfare support system.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:429:y:2015:i:c:p:76-86
    DOI: 10.1016/j.physa.2015.02.067
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    References listed on IDEAS

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    Cited by:

    1. Remuzgo, Lorena & Trueba, Carmen & Sarabia, José María, 2016. "Evolution of the global inequality in greenhouse gases emissions using multidimensional generalized entropy measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 146-157.

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