Estimation of the Unemployment Rate in Indonesia Using Nonparametric Spline Regression

Abstract

The COVID-19 pandemic impact is not only on health but on all aspects of life, one of the socio-economic aspects is unemployment. The linear regression be used as a method in modeling the unemployment rate frequently. Whereas linear regression is inappropriate for data that have trends or irregular relationship patterns such as unemployment rate data. Therefore, this research used of the nonparametric spline regression method. It can produce more flexible curves according to the flatline of data based on optimum knots at the optimum value of the cross validation. There are several factors suggested that affect the Unemployment Rate (TPT ), including the number of positive COVID cases, labor force participation rate, economic growth and human development index. The purpose of this study is to find out if there is a difference between the open unemployment rate (TPT) in Indonesia before and during the pandemic. The Data used are secondary data obtained from Badan Pusat Statistika (BPS). The calculations results show that there are differences between the models of the unemployment rate before and during the pandemic. Generally there are three significant variables in the before the pandemic model: variables of economic growth (PE) and the human development index (IPM), while during the pandemic the significant variable are the labor force participation rate (TPAK) and economic growth (PE).