Robust Spatial Regression Model in City Minimum Wages (CMW) in East Java 2018

Abstract

The minimum wage determination has always been a moment that is eagerly awaited every year by both workers and employers, even though it always creates polemics. The government seeks to raise the minimum wage every year to guarantee a decent life for workers. The fulfillment of the need for a decent living is different in each region so that it will affect the minimum wage in that area. If the minimum wage applies to city/regency areas, it is called the city minimum wage (CMW). The size of the CMW varies widely and even raises an extensive range so that outliers appear. Many factors influence the minimum wage, including Decent Living Needs (KHL), Consumer Price Index (CPI), Gross Regional Domestic Product (GRDP), labor force participation rate, per capita income, number of job seekers, number of the labor force, expenditure per capita, economic growth, and level of productivity. Modeling with regression analysis can be used to get the best model and the factors that affect CMW. Because the size of the CMW in each region is different, and there are outliers, a robust spatial regression analysis is used. This research will compare the results of the analysis of robust SAR and robust SEM using the method of moment estimation in modeling the variables that affect the city minimum wage (CMW). From the analysis, the best model is obtained from the robust SEM model, which has a smaller AIC value than the AIC value of the robust SAR model.