Mixed Geographically Weighted Regression Modeling of Gender Development Index in Indonesia

Nikma Hasanah
Dodi Vionanda
Syafriandi Syafriandi
Tessy Octavia Mukhti

UNP Journal of Statistics and Data Science
Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science

The Gender Development Index (GDI) is one of the primary measures of gender equality in the field of human development. Indonesia's GDI statistics for 2023 show the development gap between men and women. Using Mixed Geographically Weighted Regression (MGWR), a blend of regression and Geographically Weighted Regression (GWR) models, to identify the factors influencing GDI is one approach to closing the gap. The results showed that when it came to value selection using the Akaike Information Criterion (AIC), the MGWR model outperformed the GWR model. Population with health complaints and adjusted per capita expenditure were found to be globally influential factors, while female participation in parliament, open unemployment rate, and labor force participation rate were found to be locally influential factors by the MGWR model with Adaptive Kernel Bisquare weights.