中国农村多维贫困地理识别及类型划分

doi:10.11821/dlxb201506012

摘要/Abstract

摘要：

发展多维贫困度量方法和提高贫困识别精准度是近年国际贫困研究中的热点领域,也是中国未来提高农村扶贫实践质量和效率所面临的关键问题。本文借鉴国际上关于脆弱性—可持续生计框架模型在贫困研究中的学术思想,通过建立农村多维贫困测度指标体系和地理识别方法,对中国农村开展了县域尺度的贫困地理识别,并与单维度收入贫困以及国家最新认定的扶贫开发重点县进行了对比分析,最后对识别的多维贫困县按扶贫措施相似性进行了类型划分。研究结果表明：655个县级单元被识别为多维度贫困县,涉及农村人口1.41亿人;空间分布集中连片特征显著,青藏高原及其周边的南疆三地州、黄土高原西部、滇西—川西高山峡谷区为最大的连片贫困区;有71.79%的国家重点贫困县与识别结果重叠,与国家重点贫困县对比,识别的多维贫困县在各单维度和综合维度都处于更劣势水平;多维贫困县被划分为金融资本缺乏型、人力资本缺乏型、基础建设缺乏型、金融基建兼缺型、人力基建兼缺型、生计途径缺乏型、生存条件缺乏型和发展条件缺乏型8种类型。

关键词: 多维贫困, 地理识别, 脆弱性, 可持续生计, 中国农村

Abstract:

Developing methods for measuring multi-dimensional poverty and improving the accuracy of poverty identification have been the hot topics in international poverty research for decades. In light of the academic thoughts of the vulnerability and sustainable livelihood analysis framework, this paper establishes an index system and a method for geographical identification of multi-dimensional poverty, and carries out a county-level identification in rural China. Furthermore, this study makes a comparison between the identification result, income poverty and the latest designated poor regions by the Chinese government. At last, the identified multi-dimensional poor counties are classified by the similarity of poverty reduction measures. The results show that: (1) Taking the vulnerability and sustainable livelihood analysis framework proposed by DFID as theoretical basis, we build an index system of multi-dimensional poverty identification to reflect the farmers' livelihoods that multiple factors work on. It is feasible to develop a composite Multi-dimensional Development Index (MDI) for the integrated method of geographical identification of multi-dimensional poverty in rural China. (2) A total of 655 counties are identified as multi-dimensional poor counties. They are concentrated and jointly distributed in space, in which the Tibetan Plateau and its neighboring areas of three prefectures in southern Xinjiang, western Loess Plateau, mountainous and gully areas in western Yunnan and Sichuan, are suffering greatly from poverty. Besides, poor counties are mainly in Wumeng-Daliang mountainous areas, Yunnan-Guizhou-Guangxi rocky desertification areas, border mountainous areas in Yunnan, Wuling mountainous areas, Qinling-Daba mountainous areas, Shanxi-Shaanxi gully areas and Yanshan-Taihang mountainous areas. (3) In comparison to the latest designated poor counties, this paper targets at poor counties with more disadvantages at both single and multiple dimensions. Some 71.79% of designated poor counties overlap with identified poor counties. By contrast, the majority of the designated poor counties located in mountainous areas of central or eastern China do not belong to identified poor counties because of much less disadvantage/deprivation dimensions. However, the identified poor counties, which are mainly distributed in marginal areas of plateau or mountainous areas in western China, and suffering from multiple dimensions of disadvantages and deprivations, are not included in the designated poor counties. (4) According to the disadvantage/deprivation situation of different dimensions, multi-dimensional poor counties are classified into eight types, i.e., lack of financial capital, lack of human capital, lack of infrastructure, lack of both financial capital and infrastructure, lack of both human capital and infrastructure, lack of means/strategies of livelihoods, lack of living condition, and lack of development condition.

Key words: multi-dimensional poverty, geographical identification, vulnerability, sustainable livelihoods, rural China

刘艳华, 徐勇. 中国农村多维贫困地理识别及类型划分[J]. 地理学报, 2015, 70(6): 993-1007.

Yanhua LIU, Yong XU. Geographical identification and classification of multi-dimensional poverty in rural China[J]. Acta Geographica Sinica, 2015, 70(6): 993-1007.

图/表 12

图1

表1

中国农村贫困地理识别指标体系及权重分配"

类型	具体内容	贫困地理识别度量指标	权重分配	子指标	权重分配
金融资本	现金或食物净收入	纯收入水平	1	农村居民人均纯收入	1
	储蓄	—	—	—	—
	借贷	—	—	—	—
人力资本	家庭劳动力比例、年龄/性别结构、营养和健康状况	综合劳动能力	1	农村15~19岁人口比例(男性比例/女性比例)	0.33 (0.59/0.41)
	家庭劳动力比例、年龄/性别结构、营养和健康状况	综合劳动能力	1	农村20~59岁人口比例(男性比例/女性比例)	0.67 (0.59/0.41)
	受教育水平	综合教育水平	0.6	农村文盲率	0.125
				农村高中以下非文盲人口比例	0.25
				农村大专以上教育人口比例	0.625
	其它培训/技能/从业类型、个数	从业能力指数	0.4	农村农业从业人口比重	0.41
	其它培训/技能/从业类型、个数	从业能力指数	0.4	农村非农从业人口比重	0.59
自然资本	各种农用地(耕地、牧草地、园地、池塘)面积和质量	耕地综合水平	0.7	农村人均耕地面积	0.3
	各种农用地(耕地、牧草地、园地、池塘)面积和质量	耕地综合水平	0.7	单位播种面积粮食产量	0.7
	水资源可获得数量与质量	水资源水平	0.3	多年平均降雨量	1
	其它资源(风景、动植物、能矿产)可获得数量与质量	—	—	—	—
物质资本	道路、交通、供水、学校、诊所、邮局等基础和公共服务设施的可获得数量和质量	公共基础设施和服务水平	0.18	道路交通用地面积比例	1
	住房数量与质量	住房水平	0.14	农村人均住房间数	1
	牲畜及生产工具数量与质量	机械化水平	0.28	农村人均农业机械总动力	1
	家具、电器、设备等耐用品数量与质量	用电水平	0.4	农村人均用电量	1
社会资本	亲戚、朋友、邻居等密切社会关系的区位、收入、职业	城镇化水平	0.5	城镇化率	1
	参与的社会组织或团体、政治参与权的行使状况	社会支持水平	0.5	农村居民人均纯收入与城镇居民人均可支配收入比	0.7
	社会机构和过程的支持水平	社会支持水平	0.5	100-农村少数民族人口比例	0.3
环境/背景脆弱性	对各种突发的天灾人祸、季节冲击、不利的变化和趋势等的暴露度,健康状况	环境/背景脆弱水平	1	15°以上坡度面积比例	0.6
环境/背景脆弱性	对各种突发的天灾人祸、季节冲击、不利的变化和趋势等的暴露度,健康状况	环境/背景脆弱水平	1	100-农村长寿人口比例	0.4

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表3

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维度	国家扶贫开发重点县(592个)			本研究识别多维贫困县(655个)
维度	最小值	最大值	均值	最小值	最大值	均值
农民人均纯收入/元	1096	9628	3475	1096	6849	3513
人力资本得分	0.0101	0.5480	0.2929	0.0101	0.5480	0.2662
社会资本得分	0.0521	0.6779	0.2865	0.0325	0.5561	0.2522
物质资本得分	0.0202	0.2680	0.1016	0.0159	0.2564	0.0905
自然资本得分	0.0301	0.6306	0.3628	0.0168	0.6636	0.3366
环境/背景脆弱性得分	0.1108	0.8901	0.4619	0.0680	0.8901	0.5084
MDI	0.0313	2.1094	0.5943	0.0245	0.9206	0.4868

扶贫措施	弱势/剥夺维度组合¹归类	县个数	农村人口(万人)	类型名称
资助/贷款	F	53	1537.87	金融资本缺乏型
基建²	P、V、N、PV、PN、VN、PS、SV、NS、NSV、VSP	226	3938.44	基础建设缺乏型
教育培训	H	56	2616.54	人力资本缺乏型
资助/贷款+基建	FP、FPS、FNP、FPSV、FNPV、FNV、FPV、FNSV、FV、FSV	78	1779.36	金融基建兼缺型
基建+教育培训	HP、HV、HPSV、HNSV、HPS、HNPS、HNPV、HNP、HNS、HNV、HPV、HS、HN、HSV	101	1568.28	人力基建兼缺型
资助/贷款+城镇化/产业化	S、HF、HFS、HFN、FS、FN、FNS	122	1352.97	生计途径缺乏型
就地综合扶贫	无剥夺、HFPS	50	1160.92	发展条件缺乏型
搬迁+综合	FSVNP、HSVNP、FHSVNP、HFPSV、HFNPS、HFNPV、VNSP	21	146.81	生存条件缺乏型