The modeling of poverty in Java Island 2012 until 2018 using dynamic spatial panel

Abstract

Three provinces in Java had poverty percentages above the national average at 10.19% in 2020. One of the methods to identify the factors that affect poverty is the dynamic spatial panel model. This research aims to determine the best dynamic spatial panel model based on several spatial weighting matrices and identify the variables that affect the percentage of poverty in Java Island. The weighted matrices compared in this research were queen contiguity, two nearest neighbors, inverse distance, and exponential distance. The data used in this research were the percentage of poverty as a response variable, meanwhile the gross domestic product, the percentage of education completed by elementary school, the literacy rate, expenditure per capita, percentage of the productive age population, expected of length school, the average of length school, and percentage of health centers as predictor variables observed in 2012-2018 on Java Island. The methods used were data exploration, dependency test, and dynamic spatial panel analysis. The spatial autocorrelation test results show a spatial dependency on the dependent variable and predictors, so the Durbin spatial model was used. The best model was a dynamic spatial Durbin panel with a fixed effect and two nearest neighbor weighting matrices. Predictors that significantly affect poverty were the literacy rate, percentage of the productive age population, and the percentage of health centers. The marginal effect shows that an increase of gross domestic product, the percentage of education completed by elementary school, the literacy rate, and expenditure per capita could reduce the percentage of the city in Java.