Determinants of Multidimensional Poverty for Rural Households in Vietnam With Bayesian Belief Network Model

The implementation of multidimensional poverty has emerged as a novel strategy in Vietnam throughout the period spanning from 2016 to 2020. This represents a novel framework for evaluating poverty beyond the constraints associated with income‐based measurement. This research aims to ascertain the determinants that influence the multidimensional poverty experienced by rural households in Hoa Binh province, Vietnam, and to provide potential remedies for mitigating the multidimensional poverty of rural households within the designated study region. Using Binary Logistic Regression and Bayesian Network model, data analysis was conducted on a sample of 200 rural households in Hoa Binh province, Vietnam. The findings of this study revealed six significant factors that have a notable impact on the multidimensional poverty of households in Hoa Binh province. These factors have been ranked in order of importance as follows: (1) education of household heads; (2) road conditions; (3) ethnicity of household heads; (4) dependency ratio; (5) age of household heads; (6) agroforestry land area. The findings of this study can provide a foundation for policy recommendations aimed at assisting rural households in the specific study area, as well as in the broader northern mountainous provinces of Vietnam, in overcoming multidimensional poverty.