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中国多维相对贫困调查:来自南岭瑶族聚居区的证据

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area.

作者信息

Luo Huanqi, Shu Yanfei, Cai Zhaoyang

机构信息

School of Economics, Minzu University of China, No. 27 Zhongguancun South Street, Haidian District, Beijing, 100081 People's Republic of China.

School of Humanities and Law, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang, 110169 People's Republic of China.

出版信息

Environ Dev Sustain. 2022 Jul 23:1-14. doi: 10.1007/s10668-022-02570-6.

Abstract

China has made remarkable achievements in solving absolute poverty and entered into the stage of solving relative poverty which takes on multidimensional characteristics. Solving multidimensional relative poverty is the key to promoting social equity and achieving coordinated development. Based on the data of ten counties in the Nanling Yao ethnic group area in China from 2011 to 2018, we used the entropy weight method to study the degree, main influencing dimensions and distribution of multidimensional relative poverty. We found that external risk contributed the most to multidimensional relative poverty, followed by internal risk, economic development opportunity and potential development opportunity. Counties with moderate multidimensional relative poverty accounted for the largest proportion, multidimensional relative poverty was not significantly alleviated from 2011 to 2018, but some counties have different multidimensional relative poverty levels. In order to solve the multidimensional relative poverty, it is necessary to establish a Nanling economic cooperation zone, develop characteristic industries and focus on supporting counties with deep multidimensional relative poverty.

摘要

中国在解决绝对贫困方面取得了显著成就,并进入了解决具有多维特征的相对贫困阶段。解决多维相对贫困是促进社会公平和实现协调发展的关键。基于2011年至2018年中国南岭瑶族聚居区十个县的数据,我们运用熵权法研究了多维相对贫困的程度、主要影响维度和分布情况。我们发现,外部风险对多维相对贫困的贡献最大,其次是内部风险、经济发展机会和潜在发展机会。多维相对贫困程度中等的县所占比例最大,2011年至2018年多维相对贫困状况没有得到显著缓解,但部分县的多维相对贫困水平存在差异。为解决多维相对贫困问题,有必要建立南岭经济合作区,发展特色产业,并重点扶持多维相对贫困程度较深的县。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c24f/9308030/f6909e580e15/10668_2022_2570_Fig1_HTML.jpg

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