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。

Rural Poverty of Wangqing County Based on HLM and GWR

It is the precondition to achieve targeted poverty alleviation to find out the reason why people fall into poverty. Taking a remote minority-inhabited county, Wangqing, Jilin Province, China, as a study area, this article analyses the influencing factors of the annual net income of poor household at two levels: household-level and village-level by using multilevel analysis in HLM 6.8. In addition, the spatial heterogeneity of village-level characteristics is analyzed as part of understanding the geography of poverty. And the spatial heterogeneity of the village-level variables is checked by GWR. The results indicate that: 1) Internal household attributes and external environmental characteristics determine household poverty simultaneously, but internal factors dominate. 87.02% of the difference in annual net income of poor household is caused by the differences in characteristics at household-level. The remaining 12.98% is due to the differences in village-level environmental characteristics. However, the chi-square test of the estimated between-village variance component is proved to be highly significant, so the impact of environmental variables cannot be ignored. 2) Excluding the influence from village-level variables, excepting for the age, ethnicity and disability status of household heads, the gender, education level, disease status, labor capacity of household heads and the household size, dependency ratio, education burden, social relief and off-farm work at household-level are significantly associated with the annual net income of poor household. Female-headed households have lower income than male-headed households.And with the improvement of education level of household heads, income increases. The relationship between physical status of household heads and income is evident. As their health deteriorates, their incomes decrease. The disability status of household heads is also negative with income, but the results of t-tests are not significant. Labor capacity of household heads plays an important role in income, and the results show that household heads with normal abilities to work can get more income than household heads without normal abilities to work. In the study, household size, social relief and off-farm working increases the income of poor households. However, both dependency ratio and education burden decrease the income of poor households significantly. 3) Village-level variables can explain the difference of influence effect of household-level variables to different extent. The variance effect of ethnicity of household head is most affected by village-level variables. And the variance effects of the dependency ratio, disability status, gender and household size can also be highly explained by village-level variables. 4) The results of GWR show that there is spatial heterogeneity in the impacts of arable land, distance to county and average altitude from village-level. And the influencing direction and intensity of these 3 variables are different at different units. Based on the results, we suggest: Firstly the government of Wangqing county should formulate and implement poverty reduction strategies, adhere to the principle of "implementing policies for each household" in, and give attentio to "implementing policies for each village". Secondly, the government should improve the construction level of village-level education and medical service facilities, and reduce the threshold and cost of using public service facilities for poor households. More local employment opportunities should be provided by improving the employment environment. Lastly, the government should strengthen the regional coordination in spatial poverty governance, especially for the provincial-level border areas of non-centralized poverty-stricken areas.