Entropy-based China income distributions and inequality measures.
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, w...
| Publicado en: | China Economic Journal Vol. 12; no. 3; pp. 352 - 369 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=139257743&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 139257743 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 17538963 5EVV jtl: China Economic Journal issn: 17538963 maglogo: N pubinfo: dt: Oct2019 vid: 12 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 139257743 10.1080/17538963.2019.1570620 ppf: 352 ppct: 17 formats: tig: atl: Entropy-based China income distributions and inequality measures. aug: au: Fu, Qiuzi Villas-Boas, Sofia B. Judge, George affil: International Department, the People's Bank of China, Beijing, China Agricultural and Resource Economics, University of California, Berkeley, CA, USA Graduate School and Giannini Foundation, University of California Berkeley, Berkeley, CA, USA su: China Income inequality Income Probability density function Gini coefficient Maximum entropy method sug: subj: Income inequality Income China Probability density function Gini coefficient Maximum entropy method keyword: cressie-read divergence entropy maximization Income probability distribution function information theoretic methods micro-income data Pareto's law cressie-read divergence entropy maximization Income probability distribution function information theoretic methods micro-income data Pareto's law ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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