Abstract
In this paper, we construct an illustrative multidimensional poverty index for China and compare it with income poverty using the panel data from multiple waves of the China Health and Nutrition Survey. We use first order stochastic dominance method and regression analysis to test the stability of multidimensional poverty measures and probe the often-observed mis-match between multidimensional measures and income measures. We find as expected that China’s multidimensional poverty is significantly higher in rural areas and in the less developed western provinces. But relative to the income poverty, the multidimensional poverty is less volatile. Also, the ranking of provinces by income and multidimensional poverty varies. The multidimensional poverty measures are somewhat sensitive to the large change of weight, but if we control the indicators’ weight, then the multidimensional poverty measures are stable to a change of indicators.
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Notes
The PPP data is from: http://siteresources.worldbank.org/ICPEXT/Resources/ICP_2011.html.
For examples of governments having both official poverty statistics see, for example, Ecuador, Colombia, El Salvador, Costa Rica, Chile, and Bhutan. See Alkire et al. (2015) chapter 1.
The sources and introduction of data are from CHNS website: http://www.cpc.unc.edu/projects/china.
The introduction of sampling procedure is from CHNS website: http://www.cpc.unc.edu/projects/china/about/proj_desc/survey.
In China, although the compulsory education law is issued in 1986, but there is no uniform time to change the years of primary education from 5 to 6 years for different regions. Hence, the cutoff for the years of education follows some important Chinese multidimensional poverty researches, such as Wang and Alkire (2009), Zou and Fang (2011) and so on.
The reference and Stata computation procedures are from WHO: http://www.who.int/growthref/tools/en/.
For the original household-level data, the proportion of missing data is below 1% for each dimension.
We also processed the data into panel data and made a simple comparison analysis in the following part. After processing, there were 1537 household samples left each year for panel data.
The Spearman’s rank correlation coefficients arrive at the same conclusions.
The correlation coefficients among each measures of each year are shown in the Table 13 in this paper’s Appendix.
The regression outcomes with household income controlled are not shown in this paper.
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Funding was provided by the Research project in Humanities and Social Sciences by the Ministry of Education of China (Grant No. 14YJC790026).
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Alkire, S., Fang, Y. Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China. Soc Indic Res 142, 25–64 (2019). https://doi.org/10.1007/s11205-018-1895-2
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DOI: https://doi.org/10.1007/s11205-018-1895-2