基于空间计量模型的寻甸县贫困格局与贫困影响因素研究

RESEARCH ON POVERTY PATTERN AND ITS INFLUENCING FACTORS OF XUNDIAN HUI AND YI NATIONALITY AUTONOMOUS COUNTY

关键词:  空间计量经济模型  GIS技术  贫困格局  贫困影响因素  寻甸回族彝族自治县

Key words:  spatial econometric model  GIS technology  poverty pattern  poverty influencing factors  Xundian Hui and Yi nationality autonomous county

摘要:

目的 贫困问题是当今社会的热点之一。科学揭示县级区域贫困格局、分析致贫因子，可以促进县级区域性减贫战略更好地实施。方法 文章运用多种空间计量经济模型和地理信息系统（GIS）技术，研究了国家级贫困县——云南省寻甸回族彝族自治县的贫困格局，多维度剖析该区域致贫因素。结果 （1）寻甸县农村贫困具有明显的区域性差异和贫困空间效应，其贫困发生率的分布具有一定的空间特征，并在一定程度上受到地形条件的影响，呈现一定的地域差异性。总体上，寻甸县南部、县政府所在的街道仁德及其附近地区的贫困发生率普遍较低，该县北部的联合乡、六哨乡、甸沙乡等乡镇贫困发生率居于较高水平，其主要原因是这些乡镇的区位条件和地形条件相对较差。（2）相比2014—2016年，2017年寻甸县各乡镇（街道）的贫困发生率显著降低，其原因主要是2017年该县对所有排查出的“问题户”进行整体性的高强度整改、使得2017年末该县多数贫困户得以脱贫。（3）空间误差模型（随机效应）估计结果表明，农民人均纯收入的提高、农村居民恩格尔系数的降低、农村居民人均用电量的增加、每公顷耕地农业机械总动力的增加、少数民族人口比例的降低均可促进贫困发生率下降。结论 多数因素对贫困发生率的影响强度各不相同，具有明显的空间差异性。新时期中国西南山区贫困格局、致贫因素、减贫路径及模式，亟需深入研究，这也对强化贫困地理学的研究带来了新机遇和新挑战。

Abstract:

Poverty is the focus of all countries in the world. To reveale the poverty pattern of county-level regions scientifically and analyze the poverty influencing factors can promote the poverty alleviation strategies effectively implemented. This paper analyzed the poverty pattern and the influencing factors of Xundian county, which is national-level poverty-stricken county, by applying various spatial econometric models and GIS technology. The results were showed as follows. (1) There were obvious regional differences of poverty in Xundian rural area, and poverty spatial effect also existed. Some spatial characteristics also existed in poverty incidence distribution, which was affected by the topographic conditions to some extent. And the poverty incidence distribution showed regional differences. On the whole, low poverty areas were generally concentrated in the south of Xundian County and Rende street, where the county government is located in, and the surrounding areas, and high poverty areas were generally concentrated in the north of the county, such as Lianhe village, Liushao village and Diansha village, for the poor location and poor terrain condition. (2)The poverty incidence had been significantly reduced in 2017, compared with the period of 2014-2016. The main reason was that the overall high-intensity rectification of all "problem households" was undertaken and most poor households in the county had been lifted out of poverty by the end of 2017. (3) Spatial econometric model (SEM model) (random effect) estimation results showed that many factors, such as the increase of farmers’ per capita net income, the decrease of rural Engel's coefficient, the increase of farmers' per capita electricity consumption, the increase of total power of agricultural machinery per hectare cultivated land, and the decrease in the proportion of the population of ethnic minorities, can reduce the incidence of poverty. Therefore, the impact of most factors on the incidence of poverty is different in intensity, and there are significant spatial differences. In the new period, many new issues including poverty pattern, poverty-causing factors, poverty-reducing ways and models of Southwest China need to be deeply studied. It also brings new opportunities and challenges to strengthen the study of poverty geography.