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 Online:2017-11-10 Published: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

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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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