Modeling Spatial Dependece of Poverty Based on Human Development Index and Unemployment Rate in South Sulawesi Province 2024
DOI:
https://doi.org/10.32493/sm.v7i3.54480Keywords:
Poverty, HDI, OUR, Spatial, Regression, SARMAAbstract
Poverty is one of the main problems in regional economic development that requires in depth statistical analysis to understand the factors that influence it. This study aims to model the spatial dependence of the percentage of poor people in South Sulawesi Province in 2024 by considering the influence of the Human Development Index (HDI) and the Open Unemployment Rate (OUR). The analysis was conducted using three spatial regression model approaches, namely the Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Autoregressive Moving Average Model (SARMA). The selection of the best model was based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. The results of the analysis show that the SARMA model has the smallest AIC and BIC values (AIC = 102.6484 and BIC = 109.7167), so it was selected as the best model to explain the variation in poverty between regions in South Sulawesi. Parameter estimates in the SARMA model show that HDI has a significant negative effect on the percentage of poor people ( ; ), while OUR has no significant effect ( ; ). In addition, there is spatial dependence in both the dependent variable ( ) and the error component ( ), indicating the existence of interrelated poverty between regions. Thus, the results of this study confirm that an increase in HDI plays an important role in reducing poverty levels in South Sulawesi, and indicate the need for a development policy approach that considers spatial interrelationships between regions.
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