Econometric Modelling of the Rural Poverty, Unemployment and the Agricultural Sector Using a Truncated Spline Approach with Longitudinal Data

Rural poverty and unemployment remain persistent challenges in Indonesia, particularly in regions where agricultural development is uneven and land conversion accelerates socio-economic disparities. These conditions are highly relevant because rural areas serve as the backbone of food security, labour supply, and national economic stability. This study aims to address these issues by developing a flexible analytical framework that simultaneously models three indicators of rural development—rural poverty, rural unemployment, and agricultural sector growth—using a truncated spline nonparametric regression approach with longitudinal data from 2015 to 2023. The methodological approach integrates this regression with panel data across five Indonesian regions, allowing the analysis to capture nonlinear relationships and regional variations that conventional parametric models may overlook. The results indicate that population migration, land use change, and village fund allocation are the dominant drivers of rural development indicators, with nonlinear and region-specific effects. Village funds consistently reduce poverty and unemployment, while excessive land conversion restricts agricultural sector growth. The findings contribute to theory by demonstrating the advantages of flexible nonparametric approaches in modelling rural development dynamics, and to practice by offering empirical evidence for more targeted and adaptive policy interventions to alleviate poverty, reduce unemployment, and strengthen rural resilience.