Model otoregresi simultan dan otoregresi bersyarat untuk analisis kemiskinan di Provinsi Jawa Timur

Abstract

Poverty is one of the biggest problems in Indonesia. An approach to overcome this problem is determining the factors that affect poverty usually using ordinary least square regression model. However, poverty is not only influenced by explanatory variables but also poverty at surrounding locations. Therefore, this research employed spatial autoregressive models, i.e. Simultaneously Autoregressive (SAR) and Conditional Autoregressive (CAR). Spatial weighting matrix used in this study is the contiguity matrix. The statistics used for selection criteria model are the Akaike Information Criterion (AIC), the significancy of coefficient regression and variance parameters. The results show that they have same quality for spatial autoregressive models. The factors that affect poverty are the percentage of people who did not complete primary school (SD), the percentage of people who drink another kind of water instead of drinking water, and the percentage of people that getting healthy insurance, the percentage of people that getting subsidized rice, and the percentage of people that have poverty letter. LISA shows that hotspot of poverty on the island of Madura.