干旱风沙区农村多维贫困地域分异机制研究

运用A-F法测度多维贫困,结合热点分析、地理探测器和主成分分析等方法对干旱风沙区进行农村多维贫困地域分异系统剖析。结果表明:① 农村多维贫困发生率为14.73%,住房、教育和发展维度贡献率分别高达32.25%,23.94%,15.98%,收入已不再是主要致贫维度;② 村域多维贫困表现出集中连片性和地域差异性,南部山区为热点区域,北部扬黄灌区和塬区为冷点区域;③ 深度贫困地区影响MPI地域分异的主导因素为村庄区位条件、自然环境和资源禀赋;④ 农村多维贫困呈现出自然环境限制农户发展,地理区位影响群体福利,资源丰度约束农业生产的地域分异机制。为了缓解区域多维贫困,亟需推进城乡公共服务均等化和融合发展。     

海南省连片贫困地区农户致贫风险分析

农村贫困与减贫是世界性难题,也是中国各级政府高度重视并着力解决的重大民生问题。基于农户及致贫风险的文献梳理,从区位、社会和劳动力3个要素维度构建了农户致贫风险分析的二元Logistic回归模型。采用484户农户问卷调查数据,分析了海南省连片贫困地区农户的致贫风险,提出有效减贫和持续发展对策。研究发现:① 海南连片贫困地区生态环境良好但贫困发生率较高,家庭劳动力较充裕但受教育水平较低,子女教育支出负担重,因病因残致贫比例较高,女性务工人口较多,农户自身脱贫致富的发展动力不足。② 海拔高度200 m以下、男性户主、拥有残疾或患病成员、务工人口比例低、女性务工人员占比高、以及单位劳动力供养学生数高的农户具有更大的致贫风险。③ 研究未发现女性户主、少数民族、低受教育水平户主、大型规模家庭有更高的致贫风险,女性成员比例、抚养比等因素对农户贫困影响较小。激发农户内生动力、大力发展特色化和规模化农业、增加农户就业机会、加强针对农民工、女性务工人员和病残群体的社会保障等减贫政策制定实施是实现脱贫攻坚目标的重要途径。     

基于村域视角的脱贫成效空间格局及影响机制研究——以湖南省新晃侗族自治县84个出列村为例

贫困与摆脱贫困是中国改革和发展路径选择的起点和动力来源。本研究从经济建设成效、基础与公共服务设施建设成效、生活保障与就业保障成效4个维度构建评价指标体系,对2019年新晃县脱贫成效的空间格局与影响机制进行研究。结果表明：① 2019年新晃县出列村经济建设与生活保障脱贫成效指数差异较小,“高、较高、一般成效”区域相间分布,基础与公共服务设施建设、就业保障脱贫成效具有较为明显的空间分异特征。② 新晃县出列村综合脱贫成效在空间上整体呈现出“东高西低”的格局,且与地形存在较强的相关性特征。③ 新晃县综合脱贫成效主要影响因素为患有大病及长期慢性病的脱贫人数占比、行政村距县城距离、年龄结构、户均收入高于县平均收入户数占比、劳动力状况等。村域发展基础与村域自然要素是影响脱贫成效的基础本底因素,村域人口结构是影响村域脱贫成效空间分异的关键主导因素,而村域发展潜力是巩固脱贫成效的持续推动力。     

黄土高原村域多维贫困空间异质性研究——以宁夏彭阳县为例

黄土高原属于生态环境脆弱与农村经济贫困的复合区域,研究其村域多维贫困及空间异质性有助揭示乡村贫困原因及空间格局。以宁夏彭阳县为研究区,运用A-F法对村域多维贫困进行测度,并结合空间自相关、地理探测器和回归分析方法对其空间异质性进行了系统分析。结果表明：① 研究区农户多维贫困程度较深,K = 3时,多维贫困指数（MPI）为0.045,平均剥夺份额0.361,主要致贫维度是住房、健康和教育,贡献率分别为0.263、0.245、0.227,收入维度贡献率仅占0.130;② MPI空间自相关Moran's I值为0.2,即存在正相关,呈现“南北高,中部低”的格局;③ 地理探测器结果显示行政村到镇中心的距离、村平均高程、村委会到主要河流的距离是影响MPI空间异质性的主要因子,其决定力q值分别为0.552、0.396、0.326,且在最小二乘线性回归（OLSR）和分位数回归（QR）中均通过了1%的显著性检验;④ 各因子间的相互作用形成了黄土高原农户福利缺失、基础设施落后与产业发展受阻、乡镇政府职能被削弱的村域多维贫困空间分异机制。⑤ 最后提出推进新型城镇化建设,实现公共服务均等化,从根源上解决农村医疗、住房、交通设施落后等难题的建议。     

深度贫困区多维贫困测度及影响因素研究——以甘肃省岷县东部山区为例

科学揭示深度贫困区的贫困发生机制、贫困特征及致贫因素,对于目前脱贫攻坚返贫预警机制构建和2020年后相对贫困阶段扶贫开发政策的制定具有重要意义。本文采用A-F方法,构建农村家庭收入、健康、生活质量、教育4个维度11个指标的多维贫困指标体系,基于参与式农村评估(PRA)方法,访谈调查甘肃省岷县东部山区119户农村家庭,从农村家庭户主个体特征、农村家庭特征及村庄地理位置3个因素选取9个变量,定量分析了农村家庭多维贫困特征及致贫因素。研究结果表明:(1)深度贫困区农村家庭普遍呈现出多维贫困特征,多维贫困发生率H、多维贫困指数MPI随剥夺贫困值增加呈现下降态势;(2)研究区农村家庭的主要致贫因素为学历、疾病、收入来源和人均收入;(3)户主个体特征、农村家庭特征及村庄地理位置各变量均对农村家庭的多维贫困产生影响,尤其是户主年龄、常年生病病人数与多维贫困状况呈现显著正相关,户主文化程度、人均耕地面积呈现显著负相关。     

中国村域贫困地理格局及其分异机理

贫困与地理环境之间的关系是贫困地理学理论研究的核心。本文以贫困地域系统和区域多维贫困为理论基础,构建了村域贫困化的理论分析框架,以2013年底中国精准扶贫识别的12.4万个贫困村为研究对象,运用空间自相关、核密度分析和地理探测器等方法,刻画了新时期中国贫困村的空间地理格局,定量探测了贫困村地域分异的主导因子,揭示了村域贫困化与地理环境之间的相互作用机理。结果表明：① 贫困化与地理致贫因子相互作用、相互影响,空间上两者的作用路径和表现形式复杂多样。总体上,可从自然和人文2类要素和环境、经济、社会3个维度来综合识别村域致贫因子。地理环境在贫困化过程中发挥着基础性作用,经济要素是重要的致贫因子,社会要素具有贫困放大效应。② 贫困村分布具有明显的空间集聚性特征。全国贫困村空间分布与胡焕庸线和地势三级阶梯所形成的基础地理格局具有高度一致性,村域贫困化具有明显的垂直分异特征和坡度分异特征,在空间上有1个一级核心区、5个二级核心区、7个三级核心区。③ 地形、资源禀赋、劳动力状况、交通条件和公共服务是中国村域贫困化空间分异的主导因子,且省际间各因子驱动大小差异明显。交互探测结果表明,双因子交互驱动力强于单因子作用,交互作用类型以非线性增强为主。     

欠发达农区家庭贫困的动态转变分析——以河南省为例

精准扶贫战略实施以来,以收入标准衡量的农村贫困人口大幅下降,但贫困具有多维性、动态性的特点,开展农户贫困动态变化研究对新时期农村贫困的有效治理具有重要的指导意义。论文基于2010—2018年河南农村固定观察点数据,从收入、生活条件、教育3个维度构建农户多维贫困评价指标体系,并运用马尔科夫概率转移矩阵和面板Logit模型对贫困状态类型转移及家庭陷入贫困的影响因素进行实证分析。结果表明：① 多维贫困比单维贫困(尤其是收入贫困)更能准确反映农区贫困状况;② 2010—2018年间,单维贫困发生率与多维贫困发生率之间差距不断扩大,这很大程度与教育贫困发生率大幅上升,而生活条件贫困、收入教育贫困和三维贫困明显下降有关;③ 单维贫困家庭更易转入教育贫困,多维贫困家庭更容易转入教育生活条件贫困;④ 农户贫困的发生是户主特征、家庭特征和村庄特征共同作用的结果,但不同类型贫困发生的影响因素有显著差异。与单维贫困发生相比,多维贫困发生受到更少因素的显著影响,并且各影响因素的作用更大。上述结论意味着新时期扶贫工作重点转向多维贫困的同时,应依据贫困类型制定具体扶贫措施,尤其重视教育等公共服务的供给。     

集中连片特殊困难地区村域空间贫困测度指标体系研究

在阐述多维贫困和空间贫困概念内涵及其指标基础上,提出了集中连片特殊困难地区村域空间贫困测度指标体系构建的基本原则,即强调科学性和主导性原则、重视数据的可获得性和测度的可操作性、体现减贫与反贫困的新要求、突出区域性和空间刻画能力。据此,构建了包括经济、社会、环境和政策4个维度,收入和消费、市场连通性、人口状况、学有所教、病有所医、老有所养、住有所居、劳有所得、地貌要素、自然灾害、生态安全、农业生态、粮食安全和政策的实效性共13个指标组,27个原始指标或生成指标构成的集中连片特殊困难地区村域空间贫困测度指标体系。进一步讨论了空间贫困测度指标的检验、获取方法和空间化等关键问题。     

发展地理学视角下中国多维贫困测度及时空交互特征

探索贫困监测评估指标体系及区域间贫困时空交互动态特征对当前中国可持续减贫研究具有重要意义。基于发展地理学视角,引入面板向量自回归（PVAR）模型并结合人类发展分析路径与SDGs全球指标框架识别影响中国贫困的致贫和减贫因素,以此测度多维贫困指数,进而采用探索性时空数据分析（ESTDA）方法揭示多维贫困的时空交互特征。结果表明:① 中国当前贫困监测评估的致贫因子包括农作物受灾比和社会总抚养比,减贫因子则涉及人均GDP、人均社会保障支出、人均公共卫生支出、每万人医院数、新型农村合作医疗参合率、植被覆盖率、人均教育支出、高校数量、人均科学研究与试验发展支出、人均文化事业经费。② 2007—2017年中国省域收入贫困、健康贫困、文化贫困及多维贫困状况得到显著改善,全国综合贫困程度年均下降5.67%,部分省域的不同维度贫困内部出现差异化。③ 研究期内省域间多维贫困局域空间格局表现为较强的空间动态性,并呈现由东部向中、西部增大的变化态势;省域间多维贫困指数随时间演变呈现强的空间依赖关系,形成以西北和东北为高值区向四周递减的变化格局。④ 邻接省域多维贫困交互的时空网络以负向关联为主,仅有陕西与河南、陕西与宁夏、青海与甘肃、湖北与安徽、四川与贵州、海南与广东形成空间上较强的减贫协同关系。研究成果对当前中国精准扶贫战略实施尤其是2020年后预防返贫具有重要的参考价值。     

产业扶贫视角下村域空间贫困陷阱识别与策略分析——以湘西保靖县为例

从产业扶贫视角出发,选择湖南省湘西土家苗族自治州保靖县为研究案例区,构建自然环境、地理区位、经济基础、人力资本和社会事业5个维度的村域的空间贫困陷阱测度指标体系,运用综合评价方法测度村域产业扶贫面临的空间贫困陷阱,利用核密度方法分析5个维度上空间贫困陷阱密集影响区域,并在此基础上绘制村域产业扶贫的综合空间贫困陷阱地图,提出对应的发展策略,为实现贫困地区产业发展精准帮扶提供科学依据。     

基于HLM和GWR的汪清县农村贫困成因探究

明确贫困成因是提高精准扶贫效率和稳定脱贫成效的基本前提。对偏远少数民族聚居区——吉林省汪清县农村贫困成因进行解析,采用多层线性模型(HLM)同时分析了家庭层次和环境层次因素对农村贫困家庭年纯收入的影响,并利用地理加权回归方法(GWR)探索了环境层次变量影响的空间异质性。研究结果显示： ① 汪清县贫困农户家庭层次因素对家庭年纯收入的影响强于环境层次因素,但环境层次因素的影响作用亦不可忽视; ② 剔除环境层次因素的影响后,绝大多数家庭层次因素对贫困家庭年纯收入存在显著影响; ③ 环境层次因素能够在不同程度上解释家庭层次因素影响效应的差异性; ④ 环境层次因素的影响在不同区域的作用方向和强度上存在显著差异。最后从村域发展环境、医疗就业、区域内部贫困差异方面提出改善建议。     

基于G-TOPSIS模型的贫困农户多目标发展评价——以云南福贡县为例

建立贫困农户多目标发展评价体系,实现不同发展目标下的相对贫困的精准识别与动态监测,成为新阶段扶贫开发的迫切需求。论文面向精准扶贫、乡村振兴和可持续发展战略,基于贫困农户的短期、中期和长期目标,构建基于G-TOPSIS综合评价方法的贫困农户多目标发展评价模型,结合障碍度模型揭示不同发展目标下贫困农户的发展水平、发展差距及其致贫因素,并基于地理探测器对不同发展水平农户减贫的影响因素进行探测。以云南省福贡县为例的研究区实证结果表明:① 研究区目前仍存在大量未脱贫农户,脱贫攻坚的压力依然较大,全面脱贫是福贡县当前最紧迫的发展目标;已脱贫人口距全国和全省农村居民平均发展水平还有较大差距,仍处于相对贫困状态,具有较高的贫困脆弱性,防止返贫、缓解相对贫困的任务艰巨。② 短期目标下,主要致贫因素为劳动力受教育年限、卫生厕所、安全住房、家庭人均纯收入、家庭健康状况;中长期目标下,与全国和本省相比,家庭人均纯收入、劳动力受教育年限、安全住房为主要发展短板。③ 不同发展水平贫困农户空间分布特征存在较大差异,贫困空间分异受基础设施、地形条件、经济区位、自然资源、交通区位等因素的综合影响,农户发展水平越低,空间异质性越强,受地理环境的影响越大。研究结果可为减贫与发展战略的实施与监测提供决策依据与可靠的技术决策支持。     

The Dominant Livelihood Types of Farm Households and Their Determinants in Key Ecological Function Areas

The dominant livelihood types of farm households have become increasingly differentiated in recent years, which has attracted the attention of researchers. Identifying the characteristics and driving factors of household livelihood differentiation is of great significance for revealing man-land relationship and policy making. Based on the interview data of farm households in typical villages in key ecological function areas of Ningxia Hui Autonomous Region in China, we analyzed the pattern of the dominant diversified livelihood types and the livelihood characteristics among different farm households. Then we assessed the driving forces of livelihood diversification by optimal scaling regression. The results indicated that: (1) In the study area, the dominant livelihood types show two trends of agriculturally dominant livelihood (accounting for 53.07%) and non-agriculturally dominant livelihood (accounting for 46.93%). Moreover, farm households in the agro-pastoral areas are mainly agriculturally dominated (accounting for 75.68%), while farm households in the mountainous areas are mainly non-agriculturally dominated (accounting for 66.93%). (2) The labor allocation and income source of different types of farm households are consistent with their dominant livelihood types. The farm households with agriculturally dominant livelihoods have more natural resources than those with non-agriculturally dominant livelihoods. In terms of housing conditions, farm households with agriculturally dominant livelihoods are inferior to those with non-agriculturally dominant livelihoods. (3) The farm labor, dependency ratio, agricultural income, supplemental income and locational conditions have negative impacts on the non-agricultural trend of farm household livelihood decisions, while off-farm labor, non-farm income, education level and the per capita amount of compensation have significant positive impacts on it.     

Village-level multidimensional poverty measurement in China: Where and how

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error (LSE) model and spatial econometric analysis model to identify the villages’ poverty types and poverty difference. The case study shows that: (1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang. (2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions. (3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type. (4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of poverty alleviation at all levels to mobilize all kinds of anti-poverty resources.     

Land,Labor, and Rural Development: Analyzing Participation in India's Village Dairy Cooperatives

Livestock-based livelihoods are currently being promoted by international development agencies as part of global efforts to combat poverty. India's dairy development program, organized around village cooperatives, has become an important model for such efforts. This article aims to identify household characteristics that influence membership in India's rural dairy cooperatives by comparing two villages representing different degrees of success. Utilizing logistic regression methods, data collected through a comprehensive survey of all households in the two villages are analyzed to examine (1) how variables describing animal ownership, agricultural attributes, and household labor availability contribute to explaining membership in the dairy cooperative; and (2) whether factors influencing membership differ across the two villages. Our results indicate that although agricultural property ownership influences cooperative membership in both villages, the kind of dairy animal used and labor utilized for dairying work also have a significant and context-specific effect on household participation.     

中国贫困村致贫因素分析及贫困类型划分（英文）

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error(LSE) model and spatial econometric analysis model to identify the villages' poverty types and poverty difference. The case study shows that:(1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang.(2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions.(3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type.(4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of pov-erty alleviation at all levels to mobilize all kinds of anti-poverty resources.     

农户贫困脆弱性测度及其影响因素——基于秦巴山区的实证分析

防止致贫返贫、建立脱贫长效机制是巩固拓展脱贫攻坚成果的关键落脚点。探究农户贫困脆弱性及其机制可为建立预防致贫返贫机制提供思路和借鉴。通过构建贫困脆弱性分析框架和测度体系,以秦巴山区为例,测度农户贫困脆弱性水平,分析贫困脆弱性差异,采用分位数回归模型揭示农户贫困脆弱性的影响因素。结果表明：① 农户贫困脆弱性水平均值为0.046,贫困脆弱性等级呈现“纺锤形”分布。② 农户贫困脆弱性水平及不同维度间差异明显。补贴依赖型、务农主导型农户受健康冲击或教育压力大且适应力薄弱,贫困脆弱性较高。多元型和纯务工型农户具有低风险与低敏感性,适应力较高,贫困脆弱性较低。③ 农户的暴露风险、适应力具有地域分异性,中山区农户自然风险较高且高贫困脆弱性的农户比例大;河谷川塬区农户的适应力较高。④ 建档立卡贫困识别与贫困脆弱性评估结果具有一定差异。⑤ 农户贫困脆弱性受家庭层面的户主受教育程度、健康水平、职业类型、社会连接度、政策依赖性、非农就业人数、生计多样性以及村域层面的地形起伏度、道路可达性、与河流的距离以及教育可及性等因素的影响。