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河南省县域贫困程度及影响因素分析    

THE ANALYSIS OF COUNTY POVERTY DEGREE AND THE INFLUENCING FACTORS OF HENAN PROVINCE

文献类型:期刊文献

中文题名:河南省县域贫困程度及影响因素分析

英文题名:THE ANALYSIS OF COUNTY POVERTY DEGREE AND THE INFLUENCING FACTORS OF HENAN PROVINCE

作者:杨慧敏[1,2];罗庆[2];李小建[1,2]

第一作者:杨慧敏

机构:[1]河南大学环境与规划学院;[2]河南财经政法大学中原经济区"三化"协调发展河南省协同创新中心

第一机构:河南大学环境与规划学院,开封475004

年份:2017

卷号:32

期号:5

起止页码:48-55

中文期刊名:人文地理

外文期刊名:Human Geography

收录:CSTPCD;;北大核心:【北大核心2014】;CSSCI:【CSSCI2017_2018】;

基金:国家自然科学基金项目(41201131;41471117);河南省高等学校青年骨干教师资助计划(2014GGJS-078)

语种:中文

中文关键词:贫困程度;潜在贫困县;影响因素;有序logit回归模型;河南省

外文关键词:the degree of poverty; the potential poverty county; influencing factors; ordinal logistic regression model; Henan Province

摘要:贫困地区的地理识别,是区域扶贫政策精准、有效实施的前提。以经济指标衡量河南省县域相对贫困程度,并借助有序logit回归模型分析社会经济发展、自然条件、区位交通、历史基础4类变量16个影响因子对区域贫困状况的影响程度。分析发现,河南省县域综合经济发展水平存在较大的区域差异,经济发展水平相对较低的县域多分布在豫西和豫东南,且区域内存在部分潜在贫困县。回归分析结果表明,工业化水平、居民储蓄水平、气温、湿度、到最近城市的距离、国道道路密度、期初经济水平7个因子对县域贫困程度影响显著。地区扶贫政策实施过程中,在重视经济因素的减贫效应时,也应重视自然因素的致贫效应,政府主导,因地制宜,逐步实现区域协调发展。
In this paper, the relative poverty degree of the county in Henan Province is measured by the eco- nomic indices, that's the per capita net income of rural residents, per capita GDP and per capita public finance budget, and based on the measurement we make a classification of the county poverty level according to certain rules. The study period in this paper is from 2001 to 2014. On the basis of the classification, then by employing the ordinal logit regression model, we analysis the impacts of the influencing factors on region- al poverty situation. And this paper select 16 indices from four aspects, such as the social and economic de- velopment situations, natural conditions, regional traffic conditions and the historical basis, etc. The study shows that, there is a big difference in the level of county economic development of Henan Province, and the relatively lower ones located in the west and the southeast of Henan. Through comparative analysis, out of the national key counties for poverty alleviation and development, we found that there are also some poten- tial poverty counties. The results of the regression analysis show that there are seven factors have significant influence on the poverty situation of the counties, such as the level of industrialization, saving level of the urban and rural residents, temperature, moisture, distance to the nearest city, the density of the national road and economic development in the early stages, etc.

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