Open Unemployment Rate in 35 Indonesian Provinces During Covid-19 Affected by Population Growth Rate per Year, School Participation Rate, Life Expectancy Rate, and Classification of Javanese-Non-Java Regions

Abstract

This study aims to test and analyze the extent to which the open unemployment rate is influenced by several social factors, such as the rate of population growth per year, school participation rate, and life expectancy rate, and add a dummy variable, namely the classification of Javanese and non-Javanese regions in 35 provinces in Indonesia during the covid-19 pandemic, namely in 2021. This analysis was carried out using cross-section data obtained from the Indonesian Central Statistics Agency. Then, it was tested using statistics research methods, namely multiple linear regression tests and classical assumption test approaches. In addition, the purpose of this study is to prove Myrdal's theory of regional imbalance and regional unemployment theory. Theoretical Framework: This study covers several factors that can affect the open unemployment rate in 35 provinces in Indonesia during the COVID-19 period, such as population growth rate, school participation rate, life expectancy rate, and regional classification. Method: The research methodology used is quantitative where, starting with data collection and data processing from the central statistical agency, then determining independent and dependent variables, and then being tested for multiple linear regression least squares and classical test approaches including residual normality test, heteroskedasticity test, and multicollinearity test. Results and Discussion: From the results of the statistical test carried out, it can be concluded that every 1% increase in the open unemployment rate will be followed by a rise of 0.78% in the population growth rate, 10.9% in life expectancy, -0.046% in school participation, and -1.60% in the classification of Java and non-Javanese regions. Meanwhile, when viewed from R-squared, it can be concluded that 28.30% of variables such as population growth rate, life expectancy, school dropout rate, and classification of Java and Non-Java regions during the COVID-19 pandemic can be explained in the model.