Exploring Gender Development Inequality in Indonesia through Binary Logistic Regression

Abstract

The categorical data analysis method used to examine the influence of independent variables on the dependent variable is binary logistic regression. This analysis is applied when the dependent variable consists of two categories (dichotomous) with one or more independent variables of either categorical or continuous types. This study aims to identify the factors that have a significant influence on the Gender Development Index (GDI) at the district/city level in Indonesia in 2023, using data obtained from Statistics Indonesia (Badan Pusat Statistik, BPS). In addition, binary logistic regression analysis is employed to determine the model for Gender Development Index (GDI) cases. The results show that out of five independent variables, only four have a significant effect, namely Women as Professionals with an odds ratio (OR) of 1.031, Average Years of Schooling with an OR of 1.952, Expected Years of Schooling with an OR of 1.452, and Female Labor Force Participation Rate with an OR of 1.072. The independent variable with the most significant influence is average years of schooling, as it has the highest odds ratio value. This study is limited to the use of district/city-level data in Indonesia for the year 2023. The findings are particularly important because binary logistic regression has not previously been applied to analyze the Gender Development Index at the district/city level in Indonesia.