Application of Robustness Analysis in Rural Poverty-Stricken Family Return-to-Poverty Risk Warning

Abstract

Abstract Measuring and warning the risk of returning to poverty in rural poverty-stricken families is a crucial means to prevent and reduce poverty relapse, and it is also an important indicator for evaluating the effectiveness of poverty alleviation policies. The robustness analysis method is applied to the measurement and warning mechanism construction of the return-to-poverty risk in rural poverty-stricken families. This method is an optimization decision-making approach under conditions of uncertainty. It can ensure that the optimization results satisfy the constraints within a certain range without the need to know the distribution of uncertain parameters or membership functions. Based on the 2020 China Family Tracking Survey data, a comprehensive indicator system is constructed, encompassing both external risks and internal capabilities. The robustness analysis method is then used to calculate the return-to-poverty risk levels of 4,477 rural poverty-stricken households. Four warning levels are defined based on the results, and corresponding warning measures are proposed. The research reveals that the return-to-poverty risk of rural poverty-stricken families follows a right-skewed distribution with significant variations and hierarchies. Rural poverty-stricken families' return-to-poverty risk is influenced by various factors, including external shocks, economic fluctuations, living conditions, and human and social aspects, with inherent connections among these factors. Differentiated and personalized assistance services, including preventive, responsive, and restorative measures, are needed for rural poverty-stricken families of different warning levels, types, or groups. This study provides a new perspective and tool for preventing and reducing the return to poverty in rural poverty-stricken families.