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

Yanhui Wang () and Leyi Qian
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Yanhui Wang: Capital Normal University
Leyi Qian: Capital Normal University

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2017, vol. 130, issue 2, No 4, 497-522

Abstract: 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.

Keywords: Participatory poverty alleviation; Poverty-stricken village identification; Participatory poverty identification; Minimum variance model; Poverty factor analysis; Entire-Village Advancement (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11205-015-1190-4

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