A Panel Data Spatial Regression Approach for Modeling Poverty Data In Southern Sumatra

Abstract

This research examines the use of spatial panel data regression approach to model poverty data in the Southern Sumatra region. The main objective of the study is to model poverty in the Southern Sumatra region using spatial panel data regression. Panel data from districts/cities in South Sumatra, Jambi, Lampung, Bengkulu, and Bangka Belitung during the 2015-2021 period were used in the analysis. The spatial panel models used in this study are panel SAR regression and panel SEM. The results show that the spatial panel data approach is better at explaining variations in poverty levels compared to non-spatial models. A significant spatial spillover effect was found, where the poverty level of an area is influenced by the conditions of its neighboring areas. The results of the analysis show that the best model to use in modeling the Poverty Percentage data in the Southern Sumatra region is the Spatial Autoregressive Fixed Effect (SAR-FE) model based on the smallest AIC and BIC values. Factors such as average years of schooling and life expectancy are proven to have a significant influence on the percentage of poverty in the SAR Fixed Effect model.