Poverty Groups Identification and Assessment of Poverty Alleviation Programs in Rural China
- https://doi.org/10.2991/assehr.k.200428.040How to use a DOI?
- rural China, rural poverty, poverty alleviation programs, accusation rule, k-medoids clustering, multiple factor analysis
This study analyzed the outcomes of 194 individuals living in rural poverty in Guizhou Province using data obtained from a poverty alleviation information system together with several machine-learning tools. First, four dimensions were abstracted from a multiple factor analysis: family background and work condition, self-development, regional context, and health status and labor capacity. Second, five heterogeneous groups were identified through k-medoids clustering based on the above dimensions. The results showed that the most significant differences among poverty groups were related to health status and household size. The effectiveness of poverty alleviation programs for the different poverty groups was then evaluated. By employing association rule mining, we showed that education assistance is effective in poverty groups in relatively ‘good’ condition but not in others, whereas the ‘Minimum Living Standard’ program failed in all groups, particularly when it was the sole program. Thus, our study indicated that additional targeted alleviation programs should be implemented to address the needs of different poverty groups, especially in terms of education assistance, medical care, and medical prevention programs.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zeng Zhen AU - Zhu Mengxian PY - 2020 DA - 2020/05/01 TI - Poverty Groups Identification and Assessment of Poverty Alleviation Programs in Rural China BT - Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR 2020) PB - Atlantis Press SP - 174 EP - 189 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200428.040 DO - https://doi.org/10.2991/assehr.k.200428.040 ID - Zhen2020 ER -