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A PPI-MVM Model for Identifying Poverty-Stricken Villages: A Case Study from Qianjiang District in Chongqing, China

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  • Yanhui Wang

    (Capital Normal University
    Louisiana State University
    Capital Normal University)

  • Leyi Qian

    (Capital Normal University
    Capital Normal University)

Abstract

To support China’s national poverty alleviation strategies, it is urgent to develop a scientific method for identifying the poverty-stricken villages and the contributing factors. Based on the anti-poverty plan of “Entire-Village Advancement” of China and the human-environment interaction perspective, the paper proposes a participatory poverty identification model that utilizes geographic information system to quantify and integrate various contributing factors for poverty at the village level. First, a set of poverty identification factors are determined from the human-environment interaction perspective. Secondly, the game theory is used to combine the participatory subjective weight method and the objective entropy method to weight the factors, and a participatory poverty identification with minimum variance model is developed to identify the poverty-stricken villages and their contributing factors. Finally, the model is applied to Qianjiang District in Chongqing, and the case study demonstrates the effectiveness of the model. The model not only identifies the poverty-stricken villages systematically but also helps guide policies for effective poverty interventions.

Suggested Citation

  • Yanhui Wang & Leyi Qian, 2017. "A PPI-MVM Model for Identifying Poverty-Stricken Villages: A Case Study from Qianjiang District in Chongqing, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 497-522, January.
    • Handle: RePEc:spr:soinre:v:130:y:2017:i:2:d:10.1007_s11205-015-1190-4
    DOI: 10.1007/s11205-015-1190-4
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    1. Yanhui Wang & Wenping Qi, 2021. "Multidimensional spatiotemporal evolution detection on China’s rural poverty alleviation," Journal of Geographical Systems, Springer, vol. 23(1), pages 63-96, January.
    2. Yanhui Wang & Jianchen Zhang, 2018. "Integrating BP and MGWR-SL Model to Estimate Village-Level Poor Population: An Experimental Study from Qianjiang, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 639-663, July.
    3. Zhixi Xu & Zhongliang Cai & Shufan Wu & Xinran Huang & Ji Liu & Junying Sun & Shiliang Su & Min Weng, 2019. "Identifying the Geographic Indicators of Poverty Using Geographically Weighted Regression: A Case Study from Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 947-970, April.
    4. Yanhui Wang & Shoujie Jia & Wenping Qi & Chong Huang, 2022. "Examining Poverty Reduction of Poverty-Stricken Farmer Households under Different Development Goals: A Multiobjective Spatio-Temporal Evolution Analysis Method," IJERPH, MDPI, vol. 19(19), pages 1-21, October.

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