POVERTY LINE, POVERTY THRESHOLD, POVERTY FORECASTING, METHODS

Abstract

In this article, the author focuses on poverty and its consequences. The SBM method was used to calculate the poverty threshold, which helped to determine the upper and lower limits of poverty and forecast the poverty rate for the next three years. The data set entries included income, education, healthcare, and basic needs from 2020 to 2022. These inputs were used to calculate the poverty threshold. The problem-solving steps included making the dataset dimensionless, finding slacks, calculating variable weights, and then calculating poverty through Y = λ Income * (Norm. Income) + … + λ Basic Needs (Norm. Basic Needs). The poverty lines (Y) were determined to be 0.527, 0.568, and 0.628 from 2020 to 2022, respectively. The next part focused on calculating the poverty limits. The upper limits (UL) were calculated as “Poverty Line + (λ max * Sum of Slacks)” and the lower limits (LL) were calculated as “Poverty Line - (λ min * Sum of Slacks).” The results from 2020 to 2022 were 1.041, 0.087, 1.010, 0.144, and 1.033, 0.300, respectively. The innovation at this stage was to find bandwidth as an indicator of the intensity of poverty. Finally, the last stage focused on forecasting the Poverty threshold. The average annual growth rate (AAGR) was used to calculate the increasing coefficient of the regression line through [(Ending Value / Beginning Value) ^ (1 / Number of Years)] – 1 formula and found to be 0.09 or 9%. By this stage, 0.685, 0.746, and 0.813 were forecasted as the poverty rates for the years 2023-2025. The kind of poverty diagnosed at this stage was progressive.