中国贫困村致贫因素分析及贫困类型划分

doi:10.11821/dlxb201710008

地理学报 ›› 2017, Vol. 72 ›› Issue (10): 1827-1844.doi: 10.11821/dlxb201710008

中国贫困村致贫因素分析及贫困类型划分

陈烨烽^1,^2,³(), 王艳慧^1,^2,³(), 赵文吉^1,^2,³, 胡卓玮^1,^2,³, 段福州^1,^2,³

1. 首都师范大学资源环境与地理信息系统北京市重点实验室,北京 100048
2. 首都师范大学三维信息获取与应用教育部重点实验室,北京 100048
3. 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地,北京 100048

收稿日期:2016-09-26 修回日期:2017-03-17 出版日期:2017-11-10 发布日期:2017-11-06
作者简介:
作者简介：陈烨烽(1991-), 男, 浙江绍兴人, 硕士生, 主要从事GIS方法与应用研究。E-mail:13648313352@163.com
基金资助:
国家自然科学基金项目(41771157);“十二五”国家科技支撑计划项目(2012BAH33B03);首都师范大学交叉科研项目;首都师范大学青年科研创新团队项目

Contributing factors and classification of poor villages in China

Yefeng CHEN^1,^2,³(), Yanhui WANG^1,^2,³(), Wenji ZHAO^1,^2,³, Zhuowei HU^1,^2,³, Fuzhou DUAN^1,^2,³

1. Beijing key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
2. Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
3. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University , Beijing 100048, China

Received:2016-09-26 Revised:2017-03-17 Published:2017-11-10 Online:2017-11-06
Supported by:
National Natural Science Foundation of China, No.41771157;Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, No.2012BAH33B03, Interdisciplinary Research Project of Capital Normal University, and Youth Innovative Research Team of Capital Normal University

摘要/Abstract

摘要：

精确度量行政村贫困特征是当前贫困精准识别及全面脱贫的国家战略需要。基于“十二五”期间全国“整村推进”贫困村数据,构建空间贫困视角下的多维贫困度量模型综合测算全国范围内贫困村的贫困程度,基于指标贡献度分解与线性回归方法挖掘贫困村的致贫因素,利用最小方差模型（LSE）区分贫困村的贫困类型,并结合空间计量分析技术揭示贫困村的空间分布分异特征,为面向2020年全面脱贫的国家战略提供辅助决策支撑。结果表明：① 中国贫困村近一半处于中度贫困,且贫困程度与贫困规模存在显著的地域性。② 通路情况较差、自然灾害频发、收入水平低下、劳动力状况不佳是中国贫困村的主要致贫因素,显著性指标包括通路率、地貌类型、遭受自然灾害频次、人均纯收入、劳动力比例、劳动力文化素质等。③ 中国贫困村整体呈多因素致贫,三因素支配型、四因素协同型、五因素联合型贫困分布相对较高。④ “整村推进”扶贫工作初见成效,对促进贫困村可持续发展起到了较好的推进作用,但贫困村具有各自贫困特征,需进一步地因地制宜,因村施策,开展针对性帮扶工作。

关键词: 贫困村, 多维贫困度量, 贫困类型, 致贫因素, 精准扶贫, 中国

Abstract:

It is a China's strategic demand to systematically and accurately measure the poverty characteristics of poverty-stricken areas from the perspective of village-level poverty. In this context, this paper constructs a multidimensional poverty evaluation model from the perspective of spatial poverty, and combines the decomposition of indicator contribution degree and linear regression method to explore poverty contributing factors. Then it adopts LSE (Least Square Error) model and geostatistics analysis model to classify the villages' poverty types and spatial distribution difference, so as to grasp the whole poverty characteristics, poverty contributing factors and poverty types of poor villages all over the country and to provide auxiliary decision-making support services for eradicate poverty in 2020. Taking 51461 villages in China as a case, we can draw some conclusions as follows: (1) The distribution of poverty-stricken villages has an obvious regional characteristic from the perspectives of poverty level and poverty size, and most poor villages are concentrated in contiguous destitute areas, showing a pattern of high in the west and low in the east. In view of poverty level, there exists an obvious olive-shaped structure of big in the middle and small on two sides, and nearly half of the poverty-stricken villages are moderately poor. (2) China's poor villages have four main poverty contributing factor dimensions, i.e. , harsh transportation conditions, frequent natural disasters, low income and poor labor force qualities, of which, the significant poverty contributing factors are road access ratio, terrain type, suffered frequency of natural disaster, per capita net income, labor force ratio, ratio of illiterate labor forces, etc. (3) China's poor villages are driven by multiple poverty types, among which, those villages with three-factor dominated, four-factor collaborative and five-factor combined types have a relatively high proportion. (4) "Entire-village Advancement" poverty reduction strategy has achieved an initial performance, improved the village's sustainable development environment and raised the income of poor population. However, each village has its own poverty characteristics, therefore, anti-poverty measures should be adjusted according to its respective local conditions.

Key words: pverty-stricken village, multidimensional poverty measurement, poverty type, poverty contributing factors, targeted measures in poverty alleviation, China

陈烨烽, 王艳慧, 赵文吉, 胡卓玮, 段福州. 中国贫困村致贫因素分析及贫困类型划分[J]. 地理学报, 2017, 72(10): 1827-1844.

Yefeng CHEN, Yanhui WANG, Wenji ZHAO, Zhuowei HU, Fuzhou DUAN. Contributing factors and classification of poor villages in China[J]. Acta Geographica Sinica, 2017, 72(10): 1827-1844.

图/表 11

图1

表1

表2

图2

图3

表3

图4

表4

图5

表5

贫困村样例统计表

所在地区	村名	VPI	贫困类型	主致贫因素
福建永定县湖坑镇	南江村	39.42	G单因素主导型	地貌类型、灾害、到最近乡镇集市距离
河南叶县水寨乡	伍刘村	35.13	L单因素主导型	劳动力文化素质、外出劳动力比例、劳动力比例
贵州西秀区杨武布依族苗族乡	补董村	47.13	GP双因素驱动型	地貌类型、灾害、收入、到最近乡镇集市距离、住房条件
河北威县方家营乡	孙家寨村	50.59	PL双因素驱动型	劳动力文化素质、到最近乡镇集市距离、劳动力比例、卫生条件
湖北长阳土家族自治县渔峡口镇	青龙村	63.40	GPL三因素支配型	通路、地貌类型、灾害、劳动力比例、收入、到最近乡镇集市距离
湖南新宁县安山乡	安山村	55.00	PLE三因素支配型	通路、收入、劳动力比例、地貌类型、劳动力文化素质、饮水安全
贵州道真仡佬族苗族自治县洛龙镇	鹰咀村	71.96	GPLE四因素协同型	通路、地貌类型、收入、灾害、劳动力文化素质、饮水安全
广西都安瑶族自治县地苏镇	镇安村	53.23	VPLE四因素协同型	收入、通路、劳动力比例、民族村、革命老区、到最近乡镇集市距离
云南福贡县架科底乡	里吾底村	72.39	GPLME五因素联合型	收入、通路、地貌类型、劳动力文化素质、外出劳动力比例、社会保障
安徽萧县马井镇	麻堤口村	53.02	GVPLM五因素联合型	通路、灾害、劳动力比例、收入、到最近乡镇集市距离、革命老区
青海大通回族土族自治县朔北藏族乡	药匠台村	50.09	六因素综合型	地貌类型、灾害、收入、劳动力文化素质、到最近乡镇集市距离
新疆阿勒泰地区富蕴县吐尔洪乡	阔克铁热克村	49.20	六因素综合型	收入、灾害、地貌类型、民族村、边境村、到最近乡镇集市距离

表5

表6

参考文献 39

[1]	Zhang Quanhong, Zhou Qiang. On the dynamic multidimensional measurement and decomposition of China's poverty:1989-2009. The Journal of Quantitative & Technical Economics, 2014(6): 88-101.
	[张全红, 周强. 中国多维贫困的测度及分解.数量经济技术经济研究, 2014(6): 88-101.]
[2]	Lu C.Poverty and Development in China: Alternative Approaches to Poverty Assessment. New York: Routledge, 2012. doi: 10.1080/00220388.2012.739745
[3]	Jean-Yves D, Abdelkrim A, John G.Chronic and transient poverty: Measurement and estimation, with evidence from China, Journal of Development Economics, 2010, 91(2): 266-277. doi: 10.1016/j.jdeveco.2009.09.002
[4]	State Council of China. China Rural Poverty Alleviation and Development Program (2011-2020). Beijing: The State Council of China, 2011.
	[国务院. 中国农村扶贫开发纲要(2011-2020年). 北京: 中华人民共和国国务院公报, 2011.]
[5]	Tong Yuquan, Long Hualou.Study on sustainable development in the poor areas coupled with vulnerable eco-environment. China Population, Resources and Environment, 2003, 13(2): 47-51. doi: 10.3969/j.issn.1002-2104.2003.02.010
	[佟玉权, 龙花楼. 脆弱生态环境耦合下的贫困地区可持续发展研究. 中国人口·资源与环境, 2003, 13(2): 47-51.] doi: 10.3969/j.issn.1002-2104.2003.02.010
[6]	Yang S.Poverty reduction in China: The contribution of popularizing primary education. China & World Economy, 2012, 20(1): 105-122. doi: 10.1111/j.1749-124X.2012.01275.x
[7]	Deng Weijie.Classifying stricken-poverty villages and developing poverty pointedly. Rural Economy, 2013(5): 42-44.
	[邓维杰. 贫困村分类与针对性扶贫开发. 农村经济, 2013(5): 42-44.]
[8]	Grays L C, Moseley W G.A geographical perspective on poverty-environment interactions. The Geographical Journal, 2005, 171(1): 9-23. doi: 10.1111/j.1475-4959.2005.00146.x
[9]	Liu Yanhua, Xu Yong.Geographical identification and classification of multi-dimensional poverty in rural China. Acta Geographica Sinica, 2015, 70(6): 991-1007. doi: 10.11821/dlxb201506012
	[刘艳华, 徐勇. 中国农村多维贫困地理识别及类型划分. 地理学报, 2015, 70(6): 991-1007.] doi: 10.11821/dlxb201506012
[10]	Luo Qing, Fan Xinsheng, Gao Genghe, et al.Spatial distribution of poverty village and influencing factors in Qinba Mountains. Economic Geography, 2016, 36(4): 126-132.
	[罗庆, 樊新生, 高更和, 等. 秦巴山区贫困村的空间分布特征及其影响因素. 经济地理, 2016, 36(4): 126-132.]
[11]	Olivia S, Gibson J, Rozelle S, et al.Mapping poverty in rural China: How much does the environment matter? Environment and Development Economics, 2011, 16: 129-153. doi: 10.1017/S1355770X10000513
[12]	Pei Yinbao, Liu Xiaopeng, Li Yonghong, et al.Investigation and analysis of villages in extreme spatial poverty in Liupan mountain contiguous areas: A case study of Xiji, Ningxia Province. Research of Agricultural Modernization, 2015, 36(5): 748-754. doi: 10.13872/j.1000-0275.2015.0130
	[裴银宝, 刘小鹏, 李永红, 等. 六盘山特困片区村域空间贫困调查与分析: 以宁夏西吉县为例. 农业现代化研究, 2015, 36(5): 748-754.] doi: 10.13872/j.1000-0275.2015.0130
[13]	Liu Lina, Li Junjie.Research on present situation and affecting factors of poverty based on village scale in Wuling ethnic areas of Hubei Province. Journal of Huazhong Agricultural University (Social Sciences Edition), 2015(2): 126-132. doi: 10.13300/j.cnki.hnwkxb.2015.02.017
	[刘丽娜, 李俊杰. 基于村级尺度的湖北武陵民族地区贫困现状及影响因素研究. 华中农业大学学报(社会科学版), 2015(2): 126-132.] doi: 10.13300/j.cnki.hnwkxb.2015.02.017
[14]	Chen Zhongwen.Empirical research on causes of rural poverty and mechanism of poverty alleviation in mountain area: Transaction cost perspective [D]. Wuhan: Huazhong Agricultural University, 2013.
	[陈忠文. 山区农村贫困机理及脱贫机制实证研究: 一个交易成本视角[D]. 武汉: 华中农业大学, 2013.]
[15]	Wang Yanhui, Qian Leyi, Duan Fuzhou, et al.An analysis on multidimensional poverty measurement and research on its spatial distribution pattern: A case study from Neixiang County. Population & Economics, 2014(5): 114-120. doi: 10.3969/j.issn.1000-4149.2014.05.013
	[王艳慧, 钱乐毅, 段福洲, 等. 村级贫困人口多维测算及其贫困特征分析: 以河南省内乡县为例. 人口与经济, 2014(5): 114-120.] doi: 10.3969/j.issn.1000-4149.2014.05.013
[16]	Zhao Ying.Research on the spatial poverty trap of concentrated contiguous areas with particular difficulties on basis of the geographic capital: Taking Longde County of Ningxia for example [D]. Yinchuan: Ningxia University, 2015.
	[赵莹. 基于地理资本的集中连片特困地区空间贫困陷阱研究: 以宁夏隆德县为例[D]. 银川: 宁夏大学, 2015.]
[17]	Liu Yansui, Zhou Yang, Liu Jilai.Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China. Bulletin of Chinese Academy of Sciences, 2016, 31(3): 269-278.
	[刘彦随, 周扬, 刘继来. 中国农村贫困化地域分异特征及其精准扶贫策略. 中国科学院院刊, 2016, 31(3): 269-278.]
[18]	[Long Hualou, Tu Shuangshuang, Ge Dazhuan.Effects of new-type urbanization on poverty alleviation and development and corresponding countermeasures. Bulletin of Chinese Academy of Sciences, 2016, 31(3): 309-319.
	[龙花楼, 屠爽爽, 戈大专. 新型城镇化对扶贫开发的影响与应对研究. 中国科学院院刊, 2016, 31(3): 309-319.]
[19]	Liu Xiaopeng, Su Shengliang, Wang Yajuan, et al.The index system of spatial poverty of village level to monitor in concentrated contiguous areas with particular difficulties. Scientia Geographica Sinica, 2014, 34(4): 447-453.
	[刘小鹏, 苏胜亮, 王亚娟, 等. 集中连片特殊困难地区村域空间贫困测度指标体系研究. 地理科学, 2014, 34(4): 447-453.]
[20]	[Chen Yefeng, Wang Yanhui, Wang Xiaolin.Measurement and spatial analysis of poverty-stricken villages in China. Geographical Research, 2016, 35(12): 2298-2308.
	[陈烨烽, 王艳慧, 王小林. 中国贫困村测度与空间分布特征分析. 地理研究, 2016, 35(12): 2298-2308.]
[21]	Wang Yanhui, Chen Yefeng.Using VPI to measure poverty-stricken villages in China. Social Indicators Research, 2016, 127(3): 1-25. doi: 10. 1007/s11205-016-1391-5. doi: 10.1007/s11205-015-0964-z
[22]	Sen A.Development as Freedom. Oxford: Oxford University Press, 1999.
	[阿马蒂亚·森. 以自由看待发展. 北京: 中国人民大学出版社, 2006.]
[23]	Yue Yingping, He Lilong.A note of academic history accurate poverty alleviation: Sen's poverty concept. On Economic Problems, 2016(12): 17-22.
	[岳映平, 贺立龙. 精准扶贫的一个学术史注角: 阿马蒂亚·森的贫困观. 经济问题, 2016(12): 17-22.]
[24]	Gobillon L, Selod H.Spatial mismatch, poverty, and vulnerable populations. Handbook of Regional Science, 2014: 93-107. doi: 10.1007/978-3-642-23430-9_7
[25]	Barbier E B.Poverty, development and environment. Environment and Development Economics, 2010, 15(6): 635-660. doi: 10.1017/S1355770X1000032X
[26]	Ou Haiyan, Huang Guoyong.An empirical analysis of natural geography environment and poverty effect: From the perspective of space poverty theory. Journal of Anhui Agricultural University (Social Sciences Edition), 2015, 24(1): 13-19.
	[欧海燕, 黄国勇. 自然地理环境贫困效应实证分析: 基于空间贫困理论视角. 安徽农业大学学报(社会科学版), 2015, 24(1): 13-19.]
[27]	Harun U.A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems, 2011, (24): 1024-1032. doi: 10.1016/j.knosys.2011.04.014
[28]	Wang Y, Qian L.A PPI-MVM model for identifying poverty-stricken villages: A case study from Qianjiang District in Chongqing, China. Social Indicators Research, 2015, 124(3). doi: 10.1007/s11205-015-1190-4. doi: 10.1007/s11205-015-1190-4
[29]	Lv Xiangting.Survey of the screening methods of comprehensive evaluation index.Co-Operative Economy & Science, 2009(3): 54.
	[吕香亭. 综合评价指标筛选方法综述.合作经济与科技, 2009(3): 54.]
[30]	Jiang Yuxi, Chi Guotai, Yan Lijun.The linear combination weights method based on maximum entropy principle. Operations Research and Management Science, 2011, 20(1): 53-59. doi: 10.3969/j.issn.1007-3221.2011.01.010
	[姜昱汐, 迟国泰, 严丽俊. 基于最大熵原理的线性组合赋权方法. 运筹与管理, 2011, 20(1): 53-59.] doi: 10.3969/j.issn.1007-3221.2011.01.010
[31]	Alkire S, Foster J.Counting and Multidimensional Poverty Measurement. OPHI Working Papers Series, 2008.
[32]	Murayama Y, Thapa R B.Spatial Analysis and Modeling in Geographical Transformation Process: GIS-based Applications. Springer, 2011.
[33]	Sun Caizhi, Wang Xueni, Zou Wei.Measurement and spatial driving type analysis for water poverty in China based on WPI-LSE model. Economic Geography, 2012, 32(3): 9-15.
	[孙才志, 王雪妮, 邹玮. 基于WPI-LSE模型的中国水贫困测度及空间驱动类型分析. 经济地理, 2012, 32(3): 9-15.]
[34]	Feng Zhiming, Yang Yanzhao, Song Yu, et al.Study on the land use structure of Chinese counties. Journal of Natural Resources, 2003, 18(5): 552-561. doi: 10.11849/zrzyxb.2003.05.006
	[封志明, 杨艳昭, 宋玉, 等. 中国县域土地利用结构类型研究. 自然资源学报, 2003, 18(5): 552-561.] doi: 10.11849/zrzyxb.2003.05.006
[35]	Hong Z.Case influence analysis of LSE-based unbiased estimated covariance matrix in multilinear model. Journal of Suzhou University, 1996, 12(2): 45-50.
[36]	Robert H.Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University Press, 2003: 211-217.
[37]	Zhang X P, Kanbnr R.What difference do polarization measures make? An application to China. Journal of Development Studies, 2001, 37(3): 85-98. doi: 10.1080/00220380412331321981
[38]	Tang Xiaomei.Study on problems and countermeasures for implementation of poverty alleviation policy of whole village advance: Take Tianzhu Village in Cangxi County as an example [D]. Chongqing: Southwest University, 2013.
	[唐小梅. 整村推进扶贫模式政策实施中的问题与对策研究: 以苍溪县天主村为例[D]. 重庆: 西南大学, 2013.]
[39]	Wang Cheng, Fei Zhihui.Study on the relative deprivation of the relocated farmer households in the process of whole-village advance: A case study of Dazhu Village. Journal of Southwest University (Natural Science Edition), 2015, 37(4): 41-46.
	[王成, 费智慧. 整村推进进程中搬迁农户相对剥夺感研究: 以重庆市合川区大柱村为例. 西南大学学报(自然科学版), 2015, 37(4): 41-46.]

维度	指标编号	指标	指标释义
地理环境(X₁)	X₁₁	到最近乡镇集市的距离	行政村到最近乡镇集市的平均距离(km)
	X₁₂	地貌类型	行政村地貌类型(平原、丘陵、山地)
	X₁₃	遭受自然灾害频次	行政村当年所遭受的自然灾害次数(次)
行政村特征(X₂)	X₂₁	贫困村类型	行政村类型(革命老区村、少数民族聚居村、边境地区村、其他)
行政村特征(X₂)	X₂₂	人口密度	行政村人口密度(人/km²)
生产生活条件(X₃)	X₃₁	人均经济用地面积	行政村人均经济用地面积(亩/人)
	X₃₂	通路率	行政村内通机动车行驶道路自然村数量占总自然村数量的比例(%)
	X₃₃	通电率	行政村内通电户数占总户数的比例(%)
	X₃₄	通电话率	行政村内通电话户数占总户数的比例(%)
	X₃₅	安全饮用水比重	行政村内使用安全饮用水户数占总户数的比例(%)
	X₃₆	卫生厕所比重	行政村内拥有卫生厕所户数占总户数的比例(%)
	X₃₇	危房比重	行政村内住危房户数占总户数的比例(%)
劳动力状况(X₄)	X₄₁	劳动力比例	行政村劳动力总数占该村总人口的比例(%)
	X₄₂	外出劳动力比例	行政村外出劳动力数量占该村劳动力总数的比例(%)
	X₄₃	劳动力文化素质	行政村初中及以上文化水平劳动力数量占该村劳动力总数的比例(%)
医疗卫生和社会保障(X₅)	X₅₁	诊所数量	行政村诊所数量(个)
	X₅₂	每千人医生数	行政村每千人医生数量(个)
	X₅₃	参加新型农村合作医疗比例	行政村参加新型农村合作医疗人数占整个行政村人数的比例(%)
	X₅₄	参加新型农村社会养老保险比例	行政村参加新型农村社会养老保险人数占整个行政村人数的比例(%)
经济发展(X₆)	X₆₁	人均纯收入	行政村每年人均纯收入情况(元/人)

维度	维度权重	指标编号	贫困程度					主观权重	客观权重	组合权重
维度	维度权重	指标编号	1				2	3	4	5
地理环境(X₁)	0.260	X₁₁	< 3	[3, 7)	[7, 11)	[11, 18)	≥18	0.189	0.001	0.146
		X₁₂	平原	丘陵	山地	—	—	0.452	0.778	0.527
		X₁₃	0	—	1	2	> 2	0.359	0.221	0.327
行政村特征(X₂)	0.075	X₂₁	否	—	一类	两类	三类	0.554	0.840	0.760
行政村特征(X₂)	0.075	X₂₂	[0, 120)	[120, 305)	[305, 703)	[703, 1001)	≥1001	0.446	0.160	0.240
生产和生活条件(X₃)	0.250	X₃₁	>7	(3, 7]	(2, 3]	(1, 2]	[0, 1]	0.263	0.000	0.069
		X₃₂	(0.95, 1]	(0.9, 0.95]	(0.8, 0.9]	[0.5, 0.8]	[0, 0.5)	0.320	0.371	0.413
		X₃₃	(0.98, 1]	(0.94, 0.98]	(0.9, 0.94]	[0.8, 0.9]	[0, 0.8)	0.214	0.021	0.074
		X₃₄	(0.95, 1]	(0.9, 0.95]	(0.8, 0.9]	[0.5, 0.8]	[0, 0.5)	0.036	0.078	0.078
		X₃₅	(0.8, 1]	(0.6, 0.8]	(0.4, 0.6]	[0.3, 0.4]	[0, 0.3)	0.110	1.042	0.338
		X₃₆	(0.6, 1]	(0.4, 0.6]	(0.3, 0.4]	[0.2, 0.3]	[0, 0.2)	0.024	0.199	0.109
		X₃₇	[0, 0.05)	[0.05, 0.15)	[0.15, 0.25)	[0.25, 0.35]	(0.35, 1]	0.114	0.137	0.151
劳动力状况(X₄)	0.199	X₄₁	(0.7, 1]	(0.6, 0.7]	(0.5, 0.6]	[0.35, 0.5]	[0, 0.35)	0.504	0.183	0.368
		X₄₂	(0.5, 1]	(0.4, 0.5]	(0.3, 0.4]	[0.2, 0.3]	[0, 0.2)	0.223	0.388	0.293
		X₄₃	(0.85, 1]	(0.65, 0.85]	(0.45, 0.65]	[0.25, 0.45]	[0, 0.25)	0.272	0.429	0.339
医疗卫生和社会保障(X₅)	0.079	X₅₁	>3	3	2	1	0	0.170	0.024	0.056
		X₅₂	>5	(3, 5]	(1, 3]	(0, 1]	0	0.346	0.005	0.080
		X₅₃	(95,100]	(90, 95]	(80, 90]	(0, 80]	0	0.213	0.168	0.178
		X₅₄	(80, 100]	(50, 80]	(30, 50]	(0, 30]	0	0.271	0.803	0.686
经济发展(X₆)	0.136	X₆₁	> 5543	(3960, 5543]	(3168, 3960]	[2300, 3168]	[0, 2300)	1	1	1

指标	通路率	地貌类型	遭受自然灾害频次	人均纯收入	劳动力比例	劳动力文化素质	外出劳动力比例	住危房比重	到最近乡镇集市的距离	参加新型农村社会养老保险的比例
贡献度(%)	14.82	12.80	9.50	8.25	7.95	6.97	6.22	5.73	5.31	4.26
平均排名	2.61	2.59	4.89	5.82	5.35	6.88	7.61	7.72	8.14	9.94
Beta	-0.220	0.164	0.168	-0.363	-0.157	-0.191	-0.158	0.116	0.038	-0.093
指标	卫生厕所比重	贫困村类型	人均经济用地面积	安全饮水比重	通电话率	人口密度	参加新型农村合作医疗比例	通电率	千人医生数	诊所数量
贡献度(%)	3.41	3.35	2.51	2.35	1.83	1.53	0.89	0.87	0.84	0.60
平均排名	10.72	11.17	12.07	13.45	13.98	15.18	17.68	17.71	17.13	19.37
Beta	-0.081	-	-0.009	-0.060	-0.086	-	-0.035	-0.041	-0.035	-0.074

贫困类型		贫困村比例(%)	G致贫概率(%)	V致贫概率(%)	P致贫概率(%)	L致贫概率(%)	M致贫概率(%)	E致贫概率(%)
单因素主导型	36.99	0.14	50.00	0.00	29.17	20.83	0.00	0.00
双因素驱动型	48.46	8.24	81.67	1.98	69.15	46.37	0.33	0.50
三因素支配型	55.67	53.33	97.64	5.69	95.41	94.80	3.28	3.18
四因素协同型	56.28	28.99	98.34	33.92	96.45	98.12	32.59	40.58
五因素联合型	54.34	8.36	99.40	79.17	98.00	99.23	65.43	58.78
六因素综合型	51.58	0.94	100.00	100.00	100.00	100.00	100.00	100.00
总计	55.08	100.00	96.63	20.59	93.71	92.09	17.64	19.36

		“整村推进”工作实施情况				泰尔系数
		已实施		正在实施	未实施	T_总	T_间	T_内
贫困程度	贫困村数量	8264	19967	23230		-	-	-
贫困程度	平均VPI得分	54.16	55.63	54.92		0.619	0.018	0.601
致贫因素	通路率贡献度(%)	13.64	14.65	14.80		0.651	0.014	0.636
	地形类型贡献度(%)	13.92	12.57	13.07		0.638		0.020	0.618
	遭受自然灾害频次贡献度(%)	9.84	9.66	9.31	0.661		0.018	0.643
	人均纯收入贡献度(%)	7.77	8.34	7.84	0.626		0.023	0.603
	劳动力比例贡献度(%)	8.30	7.95	8.08	0.663		0.019	0.644
	劳动力素质贡献度(%)	6.86	6.98	6.85	0.655		0.021	0.633
贫困类型	单因素主导型比例(%)	0.22	0.06	0.18	2.728	0.021	2.706
	双因素驱动型比例(%)	9.97	7.22	8.50	1.208		0.028	1.181
	三因素支配型比例(%)	48.71	55.49	53.11	0.745		0.016	0.729
	四因素协同型比例(%)	32.15	27.82	28.88	0.852		0.022	0.830
	五因素联合型比例(%)	8.19	8.44	8.35	1.304		0.022	1.282
	六因素综合型比例(%)	0.76	17.31	0.98	1.974		0.004	1.970

中国贫困村致贫因素分析及贫困类型划分

Contributing factors and classification of poor villages in China

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