<![CDATA[Gross Regional Domestic Product is one of the good indicators to measure the economy of a region. In this study, GRDP modeling was conducted with the independent variables of the employed workforce (X1) and the number of job training institutions (X2) using spatial regression and Ordinary Least Square (OLS). The purpose of this study was to determine the spatial regression model of GRDP with these two predictors or independent variables, to determine the interaction of the proximity of districts/cities with neighboring districts/cities, and to determine the effect of the employed workforce (X1) and the number of job training institutions (X2) on the GRDP value. The results showed that based on the Robust Lagrange Multiplier test, the appropriate spatial regression was the SAR model. The coefficient of determination and MAPE in the OLS model were 57.8% and 1.888, while in the spatial regression model were 68.72% and 0.4139. These results indicate that spatial regression is better than OLS in modeling GRDP in East Java, because the coefficient of determination of the spatial regression model was larger and the MAPE was smaller than the OLS model. The interaction of the proximity of a district/city with neighboring districts/cities indicates that the district/city economy is influenced by the economy of neighboring districts/cities. The two independent variables, namely the employed workforce (X1) and the number of job training institutions (X2) have a positive influence on the GRDP value.]]>
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