Exploring the Determinants of Poverty Gap in Sumatra Island: A Spatial Regression Approach
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Euis Shalma
Arivina Ratih Yulihar Taher
The poverty gap index is an important indicator in planning more effective and targeted policies, but it has rarely been studied. This study analyzes the distribution pattern of poverty gap and the influence of non-food per capita expenditure, open unemployment rate, and Gini ratio on poverty gap in districts/municipalities in Sumatra Island in 2023. Using cross-section data from 154 districts/municipalities, the analysis was conducted through the calculation of the global Moran index and spatial autoregressive (SAR) regression. The results show that the gap of poverty in Sumatra Island has a clustered spatial pattern. Regions with moderate to high poverty gap are concentrated in the northern, southern, and western islands, especially in Aceh, Bengkulu, South Sumatra, Lampung, and Nias, Mentawai, and Meranti Islands. In contrast, the central part of Sumatra tends to have lower poverty gap. From the regression analysis, non-food per capita expenditure has a negative effect and Gini ratio has a positive effect on poverty gap. These findings emphasize the importance of considering spatial factors in the formulation of poverty alleviation policies in Sumatra.
Anselin, L. (1988). Spatial Econometrics: Methods and Models (Vol. 4). Springer Netherlands. https://doi.org/10.1007/978-94-015-7799-1
Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Arbia, G. (2014). A Primer for Spatial Econometrics. Palgrave Macmillan UK. https://doi.org/10.1057/9781137317940
Barber, C. (2008). Notes on Poverty and Inequality. www.fp2p.org
Benoit, K. (2011). Linear Regression Models with Logarithmic Transformations.
BPS. (2013). Pengembangan Model Sosial Ekonomi: Penggunaan Metode Geographically Weighted Regression (GWR) untuk Analisis Data Sosial dan Ekonomi. Badan Pusat Statistik.
Chen, Y. (2021). An analytical process of spatial autocorrelation functions based on Moran’s index. PLoS ONE, 16(4 April). https://doi.org/10.1371/journal.pone.0249589
de Haan, J., Pleninger, R., & Sturm, J. E. (2022). Does Financial Development Reduce the Poverty Gap? Social Indicators Research, 161(1). https://doi.org/10.1007/s11205-021-02705-8
Emalia, Z., & Budiarty, I. (2022). Spatial Phenomenon of Multidimentional Poverty in Sumatera Island. Budapest International Research and Critics Institute-Journal, 5, 7488–7500. https://doi.org/10.33258/birci.v5i1.4485
Fischer, M. M., & Wang, J. (2011). Spatial Data Analysis. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-21720-3
Gedamu, W. T., Plank-Wiedenbeck, U., & Wodajo, B. T. (2024). A spatial autocorrelation analysis of road traffic crash by severity using Moran’s I spatial statistics: A comparative study of Addis Ababa and Berlin cities. Accident Analysis and Prevention, 200. https://doi.org/10.1016/j.aap.2024.107535
Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). McGraw Hill Inc.
Haughton, J., & R. Khandker, S. (2009). Handbook on Poverty and Inequality. The World Bank. https://doi.org/10.1596/978-0-8213-7613-3
Kopczewska, K. (2020). Applied Spatial Statistics and Econometrics. Routledge. https://doi.org/10.4324/9781003033219
Liu, M., Ge, Y., Hu, S., & Hao, H. (2023). The Spatial Effects of Regional Poverty: Spatial Dependence, Spatial Heterogeneity and Scale Effects. ISPRS International Journal of Geo-Information, 12(12). https://doi.org/10.3390/ijgi12120501
Massaid, A., Hanif, M., Febrianti, D., & Chamidah, N. (2019). Modelling of Poverty Percentage of Non-Food Per Capita Expenditures in Indonesia Using Least Square Spline Estimator. IOP Conference Series: Materials Science and Engineering, 546(5), 052044. https://doi.org/10.1088/1757-899X/546/5/052044
Muñoz, J. F., Álvarez-Verdejo, E., & García-Fernández, R. M. (2018). On Estimating the Poverty Gap and the Poverty Severity Indices With Auxiliary Information. Sociological Methods and Research, 47(3), 598–625. https://doi.org/10.1177/0049124115626178
Pratama, A. D., Suparta, I. W., & Ciptawaty, U. (2021). Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province. Eko-Regional: Jurnal Pengembangan Ekonomi Wilayah, 16(1), 14–28. https://doi.org/10.20884/1.erjpe.2021.16.1.1776
Pratama, A. D., Suparta, I. W., & Ratih, A. (2022). Spatial Autocolleration and Economic Convergence in Lampung Province. JEJAK, 15(1), 29–43. https://doi.org/10.15294/jejak.v15i1.34601
Ratih, A., Gunarto, T., & Murwiati, A. (2023). Is Multidimensional Poverty Different from Monetary Poverty in Lampung Province? (pp. 202–208). https://doi.org/10.2991/978-2-38476-064-0_22
Ringga, E. S. (2024). Provincial Evidence: Long-Run Impact of Human Development Indicators on Poverty Gap and Severity. Grimsa Journal of Business and Economics Studies, 1(2), 64–74. https://doi.org/10.61975/gjbes.v1i2.26
Safitri, I. Y., Tiro, M. A., & Ruliana. (2022). Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province. ARRUS Journal of Mathematics and Applied Science, 2(2), 60–72. https://doi.org/10.35877/mathscience740
Shafira, A., Kristiani, F., & Yong, B. (2023). Penerapan Metode Klasifikasi Perangkat Lunak ArcMap pada Pemetaan Penyebaran Penyakit Dengue di Bandung. Limits: Journal of Mathematics and Its Applications, 20(1), 39. https://doi.org/10.12962/limits.v20i1.9226
Tri Wandita, D., Gunarto, T., & Ratih, A. (2022). The Effect of Economic Growth on Multidimensional Poverty. Journal Research of Social, Science, Economics, and Management, 1(9). https://doi.org/10.36418/jrssem.v1i9.156
Wardani, I. K., Susanti, Y., & Subanti, S. (2021). Pemodelan Indeks Kedalaman Kemiskinan di Indonesia Menggunakan Analisis Regresi Robust. Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST), 15–23.
Widarjono, Agus. (2018). Ekonometrika : Pengantar dan Aplikasinya Disertasi Panduan EViews (5th ed.). UPP STIM YKPN.
Zhou, Y., & Liu, Y. (2022). The geography of poverty: Review and research prospects. Journal of Rural Studies, 93, 408–416. https://doi.org/10.1016/j.jrurstud.2019.01.008
