Abstract
Accurately identifying poverty-contributing factors of farmer households in an all-round way is the critical prerequisite and guarantee for taking targeted measures in poverty alleviation. From the combined perspectives of multi-level comprehensive detection and human-nature sustainable development, this study has designed a multi-level index system of household-level, village-level, and town-level, and constructed a nested three-level hierarchical linear model to examine the poverty-contributing factors of farmer households, and to reveal the significant ones and their multi-level interaction mechanism. The case test from Fugong County shows that: (1) Poverty-contributing factors are multi-level, showing both individual and background effects. 77.14% of the poverty is caused by household-level factors, 6.24% by village-level ones and 16.62% by town-level factors. (2) Significant poverty-contributing factors at different levels are different, identifying different contribution degrees to poverty gaps of farmer households. Five household-level factors show significant influence on poverty degree and account for 70.95% of the overall poverty gap among poor households, 11.70% for four village-level significant factors and 86.80% for two town-level ones, respectively. (3) Higher-level factors have different degrees of influence on the contribution difference of lower-level ones. The two town-level factors, terrain relief and town per capita annual income have explained 59.38% of the difference of village-level proportion of migrant workers’ contribution to poverty degree among towns and 89.89% of the difference of household-level per capita annual income's contribution to poverty degree among towns respectively. (4) Measures such as improving the type of access to roads, developing characteristic planting and breeding, and implementing relocation projects, can help poor households in the study area to reduce poverty. This study provides a new perspective for identifying farmers' poverty-contributing factors and technical reference and decision support for local departments to plan and implement targeted assistance and household-specific development policies.
Highlights
Poverty is a major global problem, and it is of vital importance for harmonious and sustainable development of the whole society
According to the absolute value of regression coefficients of each model, the contribution of significant poverty-contributing factors from high to low are per capita annual income of family (-1.629), ratio of family labor force (-0.144), ratio of population enrolled in urban and rural basic pension insurance (-0.122), road access type (-0.108), ratio of population enrolled in the new rural cooperative medical insurance of China (0.068) and the ratio of population with education below high middle school excepting students (0.042). 1There is a significantly negative correlation between the per capita annual income of the family and poverty degree
From random effect of model III (a) (Table 8), it can be concluded that there are significant differences among villages in the contribution of three indicators, which are ratio of population enrolled in the new rural cooperative medical insurance of China, ratio of population enrolled in urban and rural basic pension insurance and per capita annual income of family to poverty degree
Summary
Poverty is a major global problem, and it is of vital importance for harmonious and sustainable development of the whole society. As the basic livelihood unit in rural society, farmer households bear multiple risks resulting from environmental climate change and socioeconomic policies, and face multiple economic and social pressures due to changes in the surrounding ecological environment and socioeconomic development, which will undoubtedly increase the vulnerability of farmers’ livelihoods [5, 6]. I.e., economic, social, and ecological, can contribute significantly to livelihood security [5, 7, 8] It is both critical and urgent to accurately detect poverty-contributing factors of rural poor households and further improve their ability to create sustainable livelihoods, which would provide reliable guidance and technical support for solving such poverty-related problems as "why the farmer households are poor?” and "how to help them”
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