Spatial Durbin Model (SDM) is the development of the spatial autoregressive model (SAR). In the SDM model, the dependent variable and the independent variable both contain spatial effects. Spillover occurs when changes in one area cause changes in another. The spillover effect needs to be considered because it can affect the model. This study’s main objective is to estimate the effect of spillover on the economic growth of districts/cities in East Java. Per capita income can be an indicator of regional economic growth, which has a spatial effect. The variables estimated to affect economic growth include total population, number of industries, and labor force. Data analysis in this study using R software. Researchers found a positive spatial spillover of population, number of industries, and labor force on economic growth in East Java.
Regression analysis is not always a suitable solution if the analyzed data contains spatial effects. In overcoming the spatial effect on the data, a statistical method that can overcome it is needed. Spatial regression is a method used for data that has a location effect. One of the spatial regression model that can be used is the spatial Durbin error model. Spatial Durbin error model can overcome the spatial autocorrelation relationship in the independent variables and overcome the spatial error between regions. Spatial effects influence the rate of economic growth in an area, so it is different in each region. The quality of economic growth is an essential indicator in measuring the welfare of an area’s people. The Gross Regional Domestic Product (GRDP) can measure the rate of economic growth. Many factors affect the size of the GRDP, including the total workforce, the number of industries, the number of labour, general allocation funds, regional revenue, and regional expenditure. This study uses the Spatial Durbin Error Model to model GRDP and map the level of economic growth in 38 districts/cities in East Java.
The Spatial Durbin Model (SDM) is a development of the Spatial Autoregressive Model (SAR), in which the effect of spatial lag takes into account on the independent and dependent variables. In determining the parameter estimations in the SDM model, it is necessary to determine appropriate method. The estimation methods that can be used are Maximum Likelihood Estimation (MLE), Bayesian, Generalized Method of Moment, and Method of Moment (MM). In this paper, we will determine the best estimation method for obtaining the advertisement tax revenue model. We further conduct mapping the area to optimize advertisement tax revenue in Malang. From the comparative analysis process of the MLE and MM methods, the results show that the MLE method is a suitable method for estimating SDM parameters in advertisement tax revenue data in Malang City. From the variables that have been used to the model of SDM, all variables significantly affect the advertisement tax revenue. The mapping results show that the Gadang and Bandulan villages are the places with the most potential to increase advertisement tax revenue. This is because those villages are border areas in which many vehicles pass the border, and there are also many companies/industries.
Data containing spatial effects should be analyzed using a spatial model. One of the spatial models is Spatial Durbin Error Model (SDEM). In the SDEM model, the independent variable and error both contain spatial effects. Along with the implementation of regional autonomy, each region is given the freedom to regulate financing. Each area tries to explore their respective potential to get a large Regional Original Income. Taxes are an essential source of the regional revenue so that local governments try to optimize tax revenue. This paper empirically examines the factors that can maximize tax revenue in East Java and maps the potential areas to generate high taxes. Some of the factors considered include Gross Regional Domestic Product (GRDP), population, per capita income, number of industries. By using the SDEM model, it can be seen that there is a spatial relationship, where the amount of tax revenue in each region is different and is influenced by other areas.
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