Application of the Spatial Durbin Panel Model and Geographically Weighted Panel Regression on Poverty Data in West Java Province
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Abstract
Poverty is one of the priority issues in the Sustainable Development Goals. In 2024, West Java Province became the province with the second-highest number of people living in poverty in Indonesia. This study aims to identify the variables that significantly affect the percentage of people living in poverty in districts/cities of West Java Province from 2019 to 2023, using the spatial Durbin panel model and geographically weighted panel regression. The data used is secondary data on poverty indicators in West Java Province from 2019 to 2023, sourced from Statistics Indonesia of West Java. The spatial Durbin panel model developed in this study is a fixed-effects spatial Durbin panel model. The model shows that average years of schooling and expenditure per capita have significant effects. In addition, the spatial lags of the percentage of households living in appropriate housing, the percentage of the population covered by local health insurance, and average years of schooling also have significant effects. The geographically weighted panel regression model, estimated using a fixed effect panel regression with a Gaussian fixed kernel as the optimal weighting function, produces distinct models for each region. The average year of schooling is the dominant factor influencing the percentage of people living in poverty in districts/cities in West Java Province.
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Ahmaddien, I., & Susanto, B. (2020). Eviews 9: Panel Data Regression Analysis. Gorontalo City: Ideas Publishing.
Alica, S., Acikgoz, S., Dagalp, R., & Gokmen, S. (2025). Comparison of performances of heteroskedasticity tests under measurement error 3. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 74(2), 333–345.
Alvitiani, S., Yasin, H., & Mukid, MA (2019). Modeling poverty data in Central Java Province using the fixed effect spatial Durbin model. Gaussian Journal, 8(2): 220–232.
Andrianus, F., & Alfatih, K. (2023). The influence of infrastructure on poverty: a panel data analysis of 34 provinces in Indonesia. Journal of Business Economics Informatics, 5(1): 56–62.
Asnawi, R., Kindangen, P., & Engka, DSM (2020). The influence of education, the family hope program, and the decent housing program on poverty alleviation in Southeast Minahasa Regency. Journal of Regional Economic and Financial Development, 21(2): 109–126.
Azizah, LN, Pasaribu, JRS, Hutagalung, I., Purba, AA, & Sinaga, SA (2023). Analysis of the influence of GDP and unemployment on poverty in Indonesia in 2018-2022. Journal of Sharia Economics Scholar, 2(1): 25–32.
Banito, FR, Rachmad, MR, & Zulfanetti, Z. (2022). Determinants of poverty in Jambi Province. Journal of Economic Paradigm, 17(1): 189–198.
[BPS] Central Statistics Agency. (2024a). Population Development in Poverty Alleviation and Regional Development Efforts. Jakarta: Central Statistics Agency.
[BPS] Central Statistics Agency of West Java Province. (2024b). Poverty in Districts/Cities in West Java Province 2018-2023. Bandung: Central Statistics Agency of West Java Province.
Caraka, R.E., & Yasin, H. (2017). Spatial Data Panel. Ponorogo: WADE Group.
Dimitrova, D.S., Kaishev, V.K., & Tan, S. (2020). Computing the Kolmogorov-Smirnov distribution when the underlying cdf is purely discrete, mixed, or continuous. Journal of Statistical Software, 95(10): 1–42.
Fajri, G., Syafriandi, S., Amalita. N., & Martha, Z. (2023). Comparison of queen contiguity and customized weighting matrices on spatial regression to identify factors impacting poverty in East Java. UNP Journal of Statistics and Data Science, 1(3):203–210.
Febrianti, E., Susetyo, B., & Silvianti, P. (2023). Modeling crime rates in Indonesia using geographically weighted panel regression analysis. Xplore: Journal of Statistics, 12(1): 91–109.
Gao, L., Tian, Q., & Meng, F. (2023). The impact of green finance on industrial responsibility in China: empirical research based on the spatial panel Durbin model. Environmental Science and Pollution Research, 30: 61294-61410
Goodchild, M. F. (1986). Spatial Autocorrelation. Norwich. Geo Books.
Greene, W.H. (2020). Econometric analysis. 8th ed. New York: Pearson Education Limited.
Gujarati, D.N. (2003). Basic Econometrics. 4th ed. New York: McGraw-Hill/Irwin.
Handayani, L. (2023). Analysis of the influence of the unemployment rate and hdi on poverty levels in the province of Central Java. Journal of Economic Education, 12(1): 125–132.
Meutuah, M.S., Yasin, H., & Maruddani, DAI (2017). Fixed effect geographically weighted panel regression modeling for HDI in Central Java. Gaussian Journal, 6(2): 241–250.
Mirnayanti, Masinambow, VAJ, & Masloman, I. (2024). The effect of per capita expenditure, unemployment rate and average length of schooling on the number of poor people in East Java Province. Scientific Periodical Journal of Efficiency, 24(4): 1–15.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis. 5th ed. Hoboken, New Jersey, Canada: John Wiley & Sons, Inc.
Musella, G., Castellano, R., & Bruno, E. (2023). Evaluating the spatial heterogeneity of innovation drivers: a comparison between GWR and GWPR. METRON, 81(3): 343–365.
Pertiwi, MS (2023). Sustainable development goals (SDGs) and the realization of peace in the world. Journal of Social Work, 6(1): 86–89.
Prawitrisari, IW, Indarti, D., & Wijayanto, B. (2022). Analysis of the relationship between regional GDP and poverty in Bantul Regency 2004–2022. Journal of People's Economic Dynamics, 1(2): 71–85.
Puteri, R.M., & Marwan. (2023). The influence of unemployment, per capita expenditure, education, and health on poverty in West Sumatra Province. Arzusin: Journal of Management and Basic Education, 3(3): 321–340.
Salim, M.F., Satoto, T.B.T., & Danardono. (2025). Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta , Indonesia. Tropical Medicine and Health, 53(54): 1–12.
Situmeang, LE, & Hidayat, B. (2018). The effect of health insurance ownership on household catastrophic health spending in Indonesia in 2012. Indonesian Health Policy Journal: JKKI, 7(1): 1–9.
Suharyani, YD, & Djumarno. (2023). Strategic planning and sustainable development. Scientific Journal of Global Education, 4(2): 767–778.
Vega, S. H., & Elhorst, J. P. (2015). The SLX model. Journal of Regional Science, 55(3): 339–363.
Xu, F., Chi, G., Zhang, Z., & Yang, J. (2023). How does quality regional growth affect land resources dependence in China? evidence based on spatial durbin panel models. Resources Policy, 81:1–10.
Yasin, H., Hakim, AR, & Warsito, B. (2020). Spatial Regression (Application with R) Pekalongan: WADE Group National Publishing.
Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. London: MIT Press.