Modeling Poverty Levels in West Java Using Generalized Linear Models with Poisson and Negative Binomial Distributions
DOI:
https://doi.org/10.61536/ambidextrous.v3i02.431Keywords:
Akaike Information Criterion, Negative Binomial, Generalized Linear Model, Poverty in West Java, Poisson RegressionAbstract
Poverty in West Java Province remains a major challenge with 3.67 million poor people in 2024. This study aims to model the determinants of poverty using the Generalized Linear Model (GLM). This quantitative study uses secondary data from BPS from 27 districts/cities in West Java in 2024 (population census). The instrument is a data extraction sheet with the dependent variable being the number of poor people and independent variables including unemployment, education, GRDP, sanitation, and infant mortality. Analysis techniques include multicollinearity tests, Poisson GLM, Negative Binomial GLM, and model selection based on AIC. The results show that the Negative Binomial model (AIC=305.80) is better than the Poisson (infinite AIC) due to overdispersion. Significant variables are the average length of schooling (β=-0.384, p<0.001) which reduces poverty and infant mortality (β=0.523, p<0.001) which increases poverty. Conclusion: Policy priorities on education and maternal-child health are effective in reducing structural poverty in West Java.
Downloads
References
Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.
Agustina, E., Syechalad, MN, & Hamzah, A. (2019). The influence of population size, unemployment rate, and education level on poverty in Aceh Province. Darussalam Journal of Economic Perspectives, 4(2), 265–283.https://doi.org/10.24815/jped.v4i2.13022
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317–332.https://doi.org/10.1007/BF02294359
Buheji, M., Cunha, KDC, Beka, G., Mavric, B., Souza, YLDC, Silva, SSDC, Hanafi, M., & Yein, TC (2020). The extent of COVID-19 pandemic socio-economic impact on global poverty: A global integrative multidisciplinary review. American Journal of Economics, 10(4), 213–224.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Diz-Rosales, N., Lombardía, M. J., & Morales, D. (2024). Poverty mapping under area-level random regression coefficient Poisson models. Journal of Survey Statistics and Methodology, 12(2), 404–434.
Gujarati, D.N. (2004). Basic econometrics (4th ed.). McGraw-Hill.
Hardinandar, F. (2019). Determinants of poverty (Case study of 29 cities/regencies in Papua Province). REP Journal (Research on Development Economics), 4(1), 1–12.https://doi.org/10.31002/rep.v4i1.1337
Mann, J., Larsen, P., & Brinkley, J. (2014). Exploring the use of negative binomial regression modeling for pediatric peripheral intravenous catheterization. Journal of Biomedical Science and Engineering.https://doi.org/10.7243/2053-7662-2-6
Nurjati, E. (2021). The socioeconomic determinants of poverty dynamics in Indonesia. Journal of Social and Development Studies, 37(2), 71–80.
Rifkah, NR, & Nabila, R. (2021). Analysis of factors affecting poverty in Java Island. Indonesian Journal of Islamic Economics Research, 3(1), 15–26.
Rusyana, A., Kurnia, A., Sadik, K., Wigena, AH, Sumertajaya, IM, & Sartono, B. (2021). Comparison of GLM, GLMM and HGLM in identifying factors that influence the district or city poverty level in Aceh Province. Journal of Physics: Conference Series, 1863(1), 012023. https://doi.org/10.1088/1742-6596/1863/1/012023
Santi, VM, & Rahayuningsih, Y. (2023). Negative binomial regression in overcoming overdispersion in extreme poverty data in Indonesia. Pattimura International Journal of Mathematics (PIJMath), 2(2), 43–52.
Sugiyono. (2021). Quantitative, qualitative, and R&D research methods. Alfabeta.
Suryadi, F., Jonathan, S., Jonathan, K., & Ohyver, M. (2023). Handling overdispersion in Poisson regression using negative binomial regression for poverty case in West Java. Procedia Computer Science, 216, 517–523.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rachel Keshia Lovianna
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
