A Conditional Logistic Regression Model for Analyzing Unemployment Rates in West Java

Abstract

Unemployment is a critical problem faced by developing countries. It is a complex problem which creates other social and economic problems such as poverty, economic gaps, and crimes. This paper discusses the determinant factors of unemployment rates based on empirical data using the conditional logistic regression model. The model was used to analyze matched pair data using gender, age and residence as matching factors. The result showed that household status, marriage status, as well as levels of education were the determinant factors of a person being unemployed in West Java. It is also shown that the conditional logistic regression outperformed the standard logistic regression for analyzing the cause of unemployment.