A study on the measurement of poor villages from the perspective of targeted poverty alleviation—A case of Lianzhou,Guangdong province[J]. Guangdong Agricultural Sciences, 2017, 44(10): 156-165. DOI: 10.16768/j.issn.1004-874X.2017.10.025
    Citation: A study on the measurement of poor villages from the perspective of targeted poverty alleviation—A case of Lianzhou,Guangdong province[J]. Guangdong Agricultural Sciences, 2017, 44(10): 156-165. DOI: 10.16768/j.issn.1004-874X.2017.10.025

    A study on the measurement of poor villages from the perspective of targeted poverty alleviation—A case of Lianzhou,Guangdong province

    • In China,poverty is one of the most prevalent and acute social problems in the development of social economy. And the current policy of poverty alleviation established by the Chinese government is the fundamental guarantee for solving the problem of poverty. This article is based on the theory of multidimensional poverty, constructs a multidimensional poverty measurement system with the basic data of 66 poor villages in Lianzhou City, which are determined by the policy of poverty alleviation in Guangdong Province. The dimension weights of multiple dimensions are calculated through the“ A-F dual cutoff”. Besides,combined with the weight of the index calculated by AHP-EVM method,the model of comprehensive poverty in poor villages is constructed. Through the model, the 66 poor-village’s natural poverty index,social poverty index,economic poverty index and the comprehensive poverty are calculated. According to the calculation results,the poverty level is divided into four classes,the poor villages with mild-poverty accountes for 21.2%,moderate-poverty accountes for 16.7%,severe-poverty accountes for 34.8%,extreme-poverty accountes for 27.3%. The depth of poverty of Lianzhou poor villages is heavy,which severe-poverty and extreme-poverty accountes for larger proportion,and the distribution is concentrated. Therefore, making targeted poverty alleviation policy should need to fully consider the depth of poverty and spatial distribution.
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