Impact of rural transformation on rural income and poverty for sustainable development in <scp>Bangladesh</scp>: A moments‐quantile regression with fixed‐effects models Approach

Abstract This study examines the impact of rural transformation on income and poverty in Bangladesh using district‐level panel data from five rounds of the Household Income and Expenditure Survey conducted in 1995, 2000, 2005, 2010, and 2016. Two key indicators are assessed: the share of high‐value agriculture and non‐farm employment. Fixed effects and moments‐quantile regression models are employed to analyze the relationships between rural transformation, income, and poverty, accounting for education, healthcare, electricity access, and land ownership. The results show that both high‐value agriculture and non‐farm employment significantly boost rural income and reduce poverty. Geographic disparities are evident, with high‐value agriculture being most effective in agriculturally favorable regions, while non‐farm employment has a stronger impact in areas with fewer agricultural opportunities. These findings are critical in shaping policies aligned with Sustainable Development Goal 1, which seeks to eradicate poverty. The findings indicate the need for policies to prioritize high‐value agriculture and non‐farm employment to promote income growth and reduce poverty. Expanding agricultural extension services and vocational training is vital for this transformation, alongside targeted investments in infrastructure, education, healthcare, and energy access to enhance rural development and poverty alleviation across Bangladesh.