Geographically Weighted Regression (GWR) Modeling in Identifying Factors Affecting the Gender Empowerment Index in Indonesia

Sitti Masyitah Meliyana; Ansari Saleh Ahmar; Abdul Rahman

doi:10.35877/454RI.daengku4449

Authors

Sitti Masyitah Meliyana Universitas Negeri Makassar
Ansari Saleh Ahmar Universitas Negeri Makassar
Abdul Rahman Universitas Negeri Makassar

DOI:

https://doi.org/10.35877/454RI.daengku4449

Keywords:

Geographically Weighted Regression, Spatial, Gender Empowerment Index, Gender

Abstract

This study aims to analyze the factors influencing the Gender Empowerment Index (GEI) in Indonesia using the Geographically Weighted Regression (GWR) method. The variables used in this study include the proportion of women in managerial positions, women’s income contribution, the proportion of professional workers, reported health complaints, and the proportion of women in parliament. The findings indicate that, among the five independent variables examined, only two variables significantly affect the dependent variable: the proportion of women in managerial positions (X1) and the percentage of women reporting health complaints (X5). This is evidenced by their respective probability values (Pr(>F)) of 0.0045 and 0.0128, which are below the 0.05 significance threshold. This implies that X1 and X5 have a statistically significant influence in the model. The GWR model was found to be the most suitable compared to other models, with an AIC value of 186.72 and an R² of 92.03%, indicating superior model performance in capturing spatial and non-spatial effects across regions.

References

Alsos, G. A.(2021). Educational Investments and Gender Empowerment: A Path to Equality. International Journal of Gender Studies, 12(3), 231–245.
Badan Pusat Statistik (BPS). (2023). Keadaan Angkatan Kerja di Indonesia Februari 2023. Jakarta: Badan Pusat Statistik. Diakses pada 17 September 2024 dari https://www.bps.go.id/id/statistics-table/2/MjIwMCMy/tingkat-partisipasi-angkatan-kerja-menurut-jenis-kelamin.html.
BAPPENAS dan UN Women. (2020). Laporan Pembangunan Gender dan Inklusi Sosial di Indonesia. Jakarta: Kementerian Perencanaan Pembangunan Nasional/Bappenas dan UN Women.
Brunsdon, C., Fotheringham, A. S., dan Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Fotheringham, A. S., Brunsdon, C., dan Charlton, M. (2002). Geographicall Weighted Regression the analysis of spatially varying relationships (John Wiley dan Sons (ed.)).
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., dan Harris, P. (2015). Gwmodel: An R package for exploring spatial heterogeneity using geographically weighted models. Journal of Statistical Software, 63(17), 1–50. https://doi.org/10.18637/jss.v063.i17
Goulard, M., dan Voltz, M. (1992). Linear coregionalization model: Tools for estimation and choice of cross-variogram matrix. Mathematical Geology, 24(3), 269–286. https://doi.org/10.1007/BF00893750
Grasa, A. A. (1989). Econometric model selection?: a new approach advanced studies in theoretical and applied econometrics.
Johnson, L.,(2024). Global Correlations Between Gender Empowerment and Human Development. Sustainability Studies, 20(2), 312–328.
Kabeer, N. (1999). Resources, agency, achievements: Reflections on the measurement of women’s empowerment. Development and Change, 30(3), 435–464.
Lutfiani, N., dan Scolastika Mariani, (2019). Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-square. UNNES Journal of Mathematics, 5(1), 82–91. http://journal.unnes.ac.id/sju/index.php/ujmUJM8
Mar’ah, Z., Djuraidah, A., Wigena, A., dan Hamim Wigena, A. (2017). Semi-parametric Geographically Weighted Regression Modelling using Linear Model of Coregionalization. Article in International Journal of Sciences Basic and Applied Research, 34(2), 178–186. http://gssrr.org/index.php?journal=JournalOfBasicAndApplied
Meliyana, S. M., Ahmar, A. S., & Siti Nurazizah Auliah. (2025). Geographically Weighted Poisson Regression (GWPR) Model with Fixed Gaussian Kernel and Fixed Bi-square Kernel Weights. ARRUS Journal of Social Sciences and Humanities, 5(2), 896-909. https://doi.org/10.35877/soshum3812
Nakaya, T., Fotheringham, A. S., Brunsdon, C., dan Charlton, M. (2005). Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24(17), 2695–2717. https://doi.org/10.1002/sim.2129
World Health Organization. (2009). Women and health: Today's evidence, tomorrow's agenda. World Health Organization. https://www.who.int/publications/i/item/9789241563857
Zhao, Q., Fan, Q., dan Zhou, P. (2022). An integrated analysis of GWR models and spatial econometric global models to decompose the driving forces of the township consumption development in Gansu, China. Sustainability (Switzerland), 14(1). https://doi.org/10.3390/su14010281
Zhu, C., Zhang, X., Zhou, M., He, S., Gan, M., Yang, L., dan Wang, K. (2020). Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological Indicators, 117, 106654. https://doi.org/10.1016/J.ECOLIND.2020.106654