Analysis of the Determinants of Extreme Poverty in North Morowali Regency
Main
Elisabet Monduu
Suparman
Yunus Sading
Failur Rahman*
Yohan
This study aims to empirically analyse the socio-economic determinants that influence the level of extreme poverty in North Morowali Regency. The main focus of this study lies on three independent variables, namely average length of schooling, life expectancy, and percentage of poor population. Using a quantitative approach, descriptive and verifiable methods were applied to secondary data from 2020–2023 obtained from the Central Statistics Agency (BPS) and related institutions. The analysis was conducted using multiple linear regression accompanied by classical assumption tests and significance testing. The results indicate that all three independent variables significantly influence extreme poverty, with a coefficient of determination (R²) of 0.794. Partially, average years of schooling and life expectancy have a positive and significant effect on reducing extreme poverty, while the percentage of the poor population has a negative and significant effect. These findings indicate that improving the quality of education and health is key to reducing extreme poverty, while high levels of general poverty exacerbate extreme poverty. The implication of this study is the importance of evidence-based regional policy formulation that focuses on comprehensive human development. The originality of this research lies in its approach, which integrates human development indicators into a model for measuring extreme poverty at the district level specifically.
Catalán, H. E. N., & Gordon, D. (2020). The importance of reliability and construct validity in multidimensional poverty measurement: An illustration using the Multidimensional Poverty Index for Latin America (MPI-LA). The Journal of Development Studies, 56(9), 1763–1783.
Fazzio, I., Eble, A., Lumsdaine, R. L., Boone, P., Bouy, B., Hsieh, P.-T. J., Jayanty, C., Johnson, S., & Silva, A. F. (2021). Large learning gains in pockets of extreme poverty: Experimental evidence from Guinea Bissau. Journal of Public Economics, 199, 104385.
Gai, A. M., Sir, M. M., Harsono, I., & Poerwati, T. (2023). Analysis of the Influence of Educational Background, Life Expectancy and Infrastructure Maturity on Poverty Growth in Indonesia Using Quantile Regression Method. Jurnal Kewarganegaraan, 7(2), 2586–2593.
Ghozali, I. (2016). Multivariate analysis application with IBM SPSS 25 program. Semarang: Diponegoro University Publishing Agency, 4, 352.
Ginting, A. L. (2020). Dampak angka harapan hidup dan kesempatan kerja terhadap kemiskinan. EcceS: Economics Social and Development Studies, 7(1), 42–61.
Labidi, M. A., Ochi, A., & Saidi, Y. (2024). Extreme poverty, economic growth, and income inequality trilogy in Sub-Saharan Africa and South Asia: A GMM panel VAR approach. Journal of the Knowledge Economy, 15(3), 10592–10612.
Nashwari, I. P., Rustiadi, E., Siregar, H., & Juanda, B. (2017). Geographically weighted regression model for poverty analysis in jambi province. The Indonesian Journal of Geography, 49(1), 42.
Negre, M., Lakner, C., Prydz, E. B., & Mahler, D. G. (2020). How Much Does Reducing Inequality Matter for Global Poverty?
Rahmadini, G. N., & Wijaya, R. S. (2024). Analisis Pengaruh Angka Harapan Hidup, Tingkat Pengangguran Terbuka, Tingkat Partisipasi Angkatan Kerja, dan Pertumbuhan Ekonomi Terhadap Tingkat Kemiskinan di Provinsi Jawa Timur. Jurnal Revolusi Ekonomi Dan Bisnis, 7(6).
Rahmawati, D., & Sebayang, A. F. (2023). Pengaruh Jumlah Penduduk, Indeks Pembangunan Manusia dan Upah Minimum Provinsi terhadap Kemiskinan Ekstrem. Jurnal Riset Ilmu Ekonomi Dan Bisnis, 93–100.
Rambe, A. M., Nursalamah, N., Fauzi, A., & Fakhrurrozi, F. (2024). Pengaruh Angka Harapan Hidup dan Pengeluaran Per Kapita Terhadap Tingkat Kemiskinan di Kota Padangsidimpuan. Journal of Islamic Economics and Finance, 2(1), 252–266.
Sudaryati, S., Ahmad, A. A., & Suprapto, S. (2021). The Effect of Average Length of Schooling, Life Expectancy and Economic Growth on Poverty in Banjarnegara Regency 2005-2019. Eko-Regional, 16(1), 382368.
Sugiyono. (2013). Metode Penelitian Kuantitatif Kualitatif dan R&D. Alfabeta.
Suharto, E. (2009). Kemiskinan dan perlindungan sosial di Indonesia: menggagas model jaminan sosial universal bidang kesehatan. Alfabeta.
Sumeitri, A., & Destiningsih, R. (2022). Analysis of Factors Affecting Poverty in Central Java 2016-2019. MARGINAL: Journal of Management, Accounting, General Finance and International Economic Issues, 1(4), 23–40. https://doi.org/https://doi.org/10.55047/marginal.v1i4.257
Syabrina, N. P., Hardiani, H., & Mustika, C. (2021). Pengaruh pertumbuhan ekonomi, rata-rata lama sekolah dan tingkat pengangguran terhadap tingkat kemiskinan di Provinsi Jambi. E-Jurnal Perspektif Ekonomi Dan Pembangunan Daerah, 10(1), 1–10.
Todaro, M. P., & Smith, S. C. (2015). Economic development (12th ed). The George Washington University.
Widarjono, A. (2013). Ekonometrika Pengantar Dan Aplikasi Disertai Panduan Eviews Buku Edisi Keempat. Terjemahan. Jogjakarta: Upp Stim Ykpn.
Wijaya, P. Y., & Suasih, N. N. R. (2021). One Decade, 20 Percent Education Budget: How About Causality Between Education Success and Poverty. Jurnal Ekonomi Kuantitatif Terapan, 14(1), 173–189.
