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引用本文:邱玲玲,曾维忠.精准扶贫视角下县域农村收入差距问题研究——基于四川省88个贫困县的分析[J].中国农业资源与区划,2017,38(8):151~157
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精准扶贫视角下县域农村收入差距问题研究——基于四川省88个贫困县的分析
邱玲玲, 曾维忠
四川农业大学经济学院,成都 611130
摘要:
[目的]为测算四川省贫困县农村收入差距并探析其影响因素,以期为缩小其农村收入差距提出有效的对策。[方法]文章使用2013年《四川省农村贫困监测报告》数据,运用离差法对贫困县农村收入差距进行了测度,并通过分位数回归,对贫困县农村收入差距的影响因素进行了分析。[结果]受教育程度在所有分位数上均具有显著缩小农村收入差距的作用; 在0.25分位数上,劳动能力及道路交通便捷度对农村收入差距具有非常显著的缩小效应,但随着分位数的提高,道路交通便捷度对收入差距的缩小效应逐渐减小,劳动能力的收入贫困差距缩小效应也变得不再显著。[结论]无论是在农村收入差距小还是大的贫困县,实施农村教育振兴政策均有助于缩小农村收入差距; 在农村收入差距较小的贫困县,在农村地区加大道路交通基础设施投入和积极组织劳动力技能培训将有助于减小农村收入差距。
关键词:  精准扶贫 贫困县 农村收入差距 影响因素 分位数回归
DOI:10.7621/cjarrp.1005-9121.20170822
分类号:F323.8
基金项目:国家哲学社会科学基金项目“推进西南民族地区森林碳汇扶贫的政策研究”(15BJY093)
RESEARCH ON THE COUNTY-SPECIFIC RURAL INCOME GAP FROM THE PERSPECTIVE OF TARGETED POVERTY ALLEVIATION——BASED ON 88 POOR COUNTIES IN SICHUAN PROVINCE
Qiu Lingling, Zeng Wengzhong
College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Abstract:
To analyze the rural income gap of poverty-stricken counties and its impact factors in Sichuan province, ultimately finding effective solutions to narrow rural income gap,this paper measured the rural income gap of poverty-stricken counties by using deviation method, and applied Quantile regression method to analyze the impact factors. The empirical results indicated that education level had significant impacts on narrowing the rural income gap. Both labor ability and road transportation convenience had significant shrinking effects on the rural income poverty gap. However, the shrinking impacts of education level and road transportation condition on the income gap weakened as the Quantile increased. Whether the rural income gap of the poverty-stricken county was small or enormous, implementing education-promoting policies was greatly instrumental to decrease the gap. In the poverty-stricken counties where rural income gap was small, it was helpful to narrow the rural income gap by means of reinforcing investment in the road transportation infrastructure as well as actively organizing labor skill training in rural areas.
Key words:  targeted poverty alleviation  poor counties  rural income gap  impact factors  Quantile regression
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