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多维城市贫困的测量与决定因素:来自中国山东省的证据。

Measurement and determinants of multidimensional urban poverty: Evidence from Shandong Province, China.

机构信息

Faculty of Arts & Social Science, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia.

College of Politics and Law, Heze University, Heze, Shandong, China.

出版信息

PLoS One. 2024 May 17;19(5):e0300263. doi: 10.1371/journal.pone.0300263. eCollection 2024.

Abstract

China eliminated rural poverty under current poverty standards in 2020. However, compared with rural poverty, urban poverty in China has been somewhat neglected. This paper aims to discover the changes and determinants of multidimensional urban poverty in Shandong Province, a representative province in Eastern China. Using a nationally representative panel dataset, the China Family Panel Studies, and the Dual Cutoff method, this study creates a multidimensional poverty index with four dimensions and 11 indicators to measure urban poverty in Shandong Province. This paper discovers that while the incidence of multidimensional urban poverty in Shandong Province decreased from 47.62% in 2010 to 36.45% in 2018, the intensity of multidimensional poverty only decreased from 41.27% to 37.25%, which indicates the inadequacy of urban anti-poverty efforts in Shandong Province. This paper also uses logistic regression to identify the determinants of multidimensional urban poverty. The findings suggest that income, health, drinking water, and durable goods are the main determinants of multidimensional urban poverty in Shandong Province. Based on these findings, this study provides targeted recommendations for future urban anti-poverty policies in Shandong Province.

摘要

中国已于 2020 年消除了按现行贫困标准测算的农村贫困,但相较于农村贫困,中国的城市贫困问题则有些被忽视。本文旨在发现中国东部省份山东省多维城市贫困的变化和决定因素。本研究使用全国代表性的面板数据集——中国家庭追踪调查(CFPS)和双界限法,创建了一个包含四个维度和 11 个指标的多维贫困指数,用以衡量山东省的城市贫困状况。研究发现,虽然山东省多维城市贫困发生率从 2010 年的 47.62%下降到 2018 年的 36.45%,但多维贫困的强度仅从 41.27%下降到 37.25%,这表明山东省城市反贫困工作力度不足。本文还使用逻辑回归识别了多维城市贫困的决定因素。研究结果表明,收入、健康、饮用水和耐用品是山东省多维城市贫困的主要决定因素。基于这些发现,本研究为山东省未来的城市反贫困政策提供了有针对性的建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11101045/6e05c2e7b45e/pone.0300263.g001.jpg

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