Entropy-based China income distributions and inequality measures

Qiuzi Fu, Sofia Villas-Boas and George Judge ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: We use information theoretic information recovery methods, on a 2005 sample of household income data from the Chinese InterCensus, to estimate the income distribution for China and each of its 31 provinces and to obtain corresponding measures of income inequality. Using entropy divergence methods, we seek a probability density function solution that is as close to a uniform probability distribution of income (with the least inequality), as the data will permit. These entropy measures of income inequality reflect how the allocation and distribution systems are performing, and we show the advantages of investigating province variation in income inequality using entropy measures rather than Gini coefficients. Finally, we use a sample of data from the China Family Panel Study to recover an estimate of the 2010 and the 2016 to investigate possible directions of inequality changes using these different additional data sources, given that the 2015 Inter-Census is not yet available.

Keywords: Reduced Inequalities; Income probability distribution function; micro-income data; information theoretic methods; cressie-read divergence; entropy maximization; Pareto?s law; Applied Economics (search for similar items in EconPapers)
Date: 2019-09-02
New Economics Papers: this item is included in nep-cna
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Citations: View citations in EconPapers (1)

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