Pengaruh Pendapatan Perkapita dan Jumlah Penduduk Terhadap Tingkat Kemiskinan di Kabupaten Bone Bolango Provinsi Gorontalo
Main Article Content
Abstract
This study aims to analyze the effect of per capita income on poverty levels in Bone Bolango Regency, Gorontalo Province. The regency faces challenges in reducing poverty despite various government programs. The knowledge gap regarding the direct contribution of per capita income to poverty is the focus of this research. Here, we demonstrate that an increase in per capita income has a negative correlation with poverty levels, where each rise in income is associated with a significant reduction in poverty percentage. However, the study also found that population growth significantly increases poverty if not accompanied by improvements in economic and social infrastructure. The findings of this research imply that economic policies aimed at increasing income must be supported by improvements in access to education, healthcare, and infrastructure to more effectively reduce poverty rates in the region. Future research is expected to deepen the analysis of other factors that influence poverty more comprehensively
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Afzal, M., Malik, M. E., Ishrat, B., Kafeel, S., & Hina, F. (2012). Relationship among Education , Poverty and Economic Growth in Pakistan : An Econometric Analysis. Journal of Elementary Education, 22(1).
Ayu Rizaty, M. (2022). “BPS: Jumlah Penduduk Indonesia Sebanyak 275,77 Juta pada 2022”. DataIndonesia.Id.
Bank, W. (2015). World Bank Group Climate Action Plan 2021 - 2025. The World Bank Group A to Z 2016.
Bourguignon, F. (2018). Simple adjustments of observed distributions for missing income and missing people. Journal of Economic Inequality, 16(2). https://doi.org/10.1007/s10888-018-9388-8
Cruz, M., & Ahmed, S. A. (2018). On the impact of demographic change on economic growth and poverty. World Development, 105. https://doi.org/10.1016/j.worlddev.2017.12.018
Diener, E., Tay, L., & Oishi, S. (2013). Rising income and the subjective well-being of nations. Journal of Personality and Social Psychology, 104(2). https://doi.org/10.1037/a0030487
Fang, Y. (2011). Economic welfare impacts from renewable energy consumption: The China experience. In Renewable and Sustainable Energy Reviews (Vol. 15, Issue 9). https://doi.org/10.1016/j.rser.2011.07.044
Ghosh, R., Das, N., & Mondal, P. (2023). Explanation of major determinants of poverty using multivariate statistical approach and spatial technology: a case study on Birbhum district, West Bengal, India. GeoJournal, 88. https://doi.org/10.1007/s10708-022-10774-6
Harvie, D., Slater, G., Philp, B., & Wheatley, D. (2009). Economic well-being and british regions: The problem with GDP per capita. Review of Social Economy, 67(4). https://doi.org/10.1080/00346760802245383
Kavya, T. B., & Shijin, S. (2020). Economic development, financial development, and income inequality nexus. Borsa Istanbul Review, 20(1). https://doi.org/10.1016/j.bir.2019.12.002
Markowitz, J. S. (2018). Multivariate analysis. In SpringerBriefs in Public Health (Issue 9783319772028). https://doi.org/10.1007/978-3-319-77203-5_8
Mustapa, I., Sumarmo, Setiawan, B., & Pramoedyo, H. (2022). Strategies for strengthening the rural economic base in stimulating the growth of Bone Bolango Regency. Caspian Journal of Environmental Sciences, 20(5). https://doi.org/10.22124/cjes.2022.6082
Rahmawati, A. I., Ardianti, L., Husna, S. H. N., Paryani, E., Rosidhah, N. A., Amalia, T. P., & Putri, R. F. (2019). Poverty Approach and How to Reduce it with an Agropolitan Program in Gorontalo Province, Indonesia. ASEAN Journal on Science and Technology for Development, 36(2). https://doi.org/10.29037/ajstd.578
Ravallion, M. (2022). Missing Top Income Recipients. Journal of Economic Inequality, 20(1). https://doi.org/10.1007/s10888-022-09530-0
Ryumina, E. (2023). Analysis of the regional differentiation factors of indicators of electricity consumption by the population of Russia. Population, 26(3). https://doi.org/10.19181/population.2023.26.3.9
Sarkodie, S. A., & Ozturk, I. (2020). Investigating the Environmental Kuznets Curve hypothesis in Kenya: A multivariate analysis. Renewable and Sustainable Energy Reviews, 117. https://doi.org/10.1016/j.rser.2019.109481
Wooldridge, J. M. (2019). Correlated random effects models with unbalanced panels. Journal of Econometrics, 211(1). https://doi.org/10.1016/j.jeconom.2018.12.010