贫穷
脆弱性(计算)
政府(语言学)
农村贫困
经济
发展经济学
长期贫困
农村地区
经济增长
社会经济学
政治学
减贫
法学
哲学
语言学
计算机科学
计算机安全
作者
Yue Zhang,Wen‐Xiong Wang,Yanfei Feng
出处
期刊:Land Use Policy
[Elsevier]
日期:2022-03-01
卷期号:114: 105963-105963
被引量:24
标识
DOI:10.1016/j.landusepol.2021.105963
摘要
Poverty elimination poses a great challenge to all countries around the world, and the Chinese government is consistently concerned with poverty issues. Rural land consolidation (RLC) is the important measure to alleviate poverty in rural China by improving the efficiency of land use. From the perspective of future poverty, the study analyzes the mechanism by which different RLC models, the government-dominant model and the Public-Private-Partnership (PPP) model, decrease poverty vulnerability by enhancing capabilities and endowing rights under the framework of the theory of capacity poverty proposed by Amartya Sen. Based on survey data of 562 rural households from Hubei and Guizhou Provinces, this study explores the role of and differences between two RLC models in decreasing the poverty vulnerability of registered and non-registered households through the PSM-DID estimator. The main findings are as follows: (1) The proportions of poor households and poverty vulnerability households in both registered and non-registered households have declined after the implementation of RLC, revealing that poverty vulnerability occurs along with poverty; (2) Both RLC models can significantly reduce the poverty vulnerability of both types of rural households. Furthermore, the effect of reducing the poverty vulnerability of non-registered households is greater than that of registered households; (3) The PPP RLC model reduces the poverty vulnerability much more effectively than the government-led model. Based on the above findings, the paper proposes corresponding policy recommendations for improving stability of RLC for poverty alleviation, thereby decreasing the probability of future poverty. Additionally, this study provides references for the developing areas and countries to make long-acting RLC policies for poverty alleviation.
科研通智能强力驱动
Strongly Powered by AbleSci AI