Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

Abstract The examination of poverty-causing factors and their mechanisms action in poverty-stricken villages is an important topic associated with poverty reduction issues. Although the individual or background effects multilevel influencing have been considered some previous studies, spatial these are rarely involved. By considering nested geographic administrative features integrating detection individual, background, effects, a bilevel hierarchical linear model (HSLM) established this study to identify significant that cause poor villages, as well through which contribute at both village county levels. An experimental test region Wuling Mountains central China revealed following findings. (1) There were area. Moreover, 48.28% overall difference incidence resulted from level. Additionally, 51.72% (2) Poverty-causing observed different levels, featured mechanisms. Village-level accounted for 14.29% incidence, there five village-level factors. (3) regression was found be superior terms goodness fit. This offers technical support policy guidance regional development.