Poverty Modeling in East Java Province Using the Spatial Seemingly Unrelated Regression (Sur) Method

Abstract

Poverty is a complex problem because it relates to various aspects of human life. In Indonesia, there is one province that has a very high percentage of poverty, namely East Java Province. Although from year to year the poverty rate has decreased, when viewed from the national level it is still very far from the government's expectations of reducing the poverty rate. Cases of poverty can be modeled by Econometrics. Econometric models are often applied to problems involving one or more related equations. One method that can be used to solve several interrelated equations because there is a correlation error regression between one another, namely Seemingly Unrelated Regression which is usually abbreviated as SUR, in this case Spatial Seemingly Unrelated Regression (SUR-Spatial) is development that takes into account the spatial influence between locations. From the results of tests conducted in the SUR-Spatial Lagrange Multiplier model, the poverty data generated by the East Java Province is the SUR-Spatial Autoregressive Model (SUR-SAR). So with the SUR-SAR model it can be seen that the variable that has a significant effect on the percentage of poor people is the growth rate of Gross Regional Domestic Product based on the constant price of the minimum wage for each district, as well as the average length of school years. Meanwhile, the Poverty Depth Index has an effect because of the growth rate of Gross Regional Domestic Product on the basis of constant prices and the average length of schooling. The Poverty Severity Index is influenced by the growth rate of Gross Regional Domestic Product at constant prices and average years of schooling.