Extreme Poverty Trap In Kalimantan Barat

Fatmawati Fatmawati, Badan Pusat Statistik, Indonesia
Preatin Preatin, Badan Pusat Statistik, Indonesia

Abstract


Poverty is complex, conceptually and empirically, because it will affect how to understand poverty, its analysis, and policy formulation to overcome poverty. World Bank defines International Poverty Line (GK) as US$ 1.9 as it adjusts for inflation and living standards. Indonesia's poverty rate released by BPS uses a monetary approach, GK, which represents the minimum amount of money a person needs to meet food needs, equivalent to 2100 calories and other non-food needs. Ordinal regression Partial Proportional Odds Model (PPOM) found the best model with independent variables: Number of Household Members, Age, Defecation Facilities, Diplomas, Lightning, Main Activities, and Access to Clean Water, and all these variables significantly affect the status of poor both Extreme Poor, Poor, and Near Poor levels. It was found that Odds Ratio was very different at the Extreme Poor level. It was concluded that this category had different tendencies from other levels. Handling the extreme poor with various conditions requires different handling than the poor. Extremely poor with one household member, aged 1-14 years and 60 years and over, more in need of Social Assistance from the government. Extreme Poor with certain other conditions requires more research to determine the most effective alleviation program. 


Keywords


poverty, extreme poverty, ordinal regression, partial proportional odds model, odds ratio.

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References


Agresti, A. (2010). Analysis of Ordinal Categorical Data - Examples of Using R for Modeling Ordinal Data Summary of R ( and S-Plus ). Analysis, 2010, 30.

Alkire, S., & Jahan, S. (2018). The New Global MPI 2018: Aligning with the Sustainable Development Goals. United Nations Development Programme (UNDP), September.

Amida, O. V., & Sitorus, J. R. H. (2021). PENERAPAN REGRESI LOGISTIK BINER MULTILEVEL DALAM ANALISIS PENGARUH KARAKTERISTIK INDIVIDU, RUMAH TANGGA, DAN WILAYAH TERHADAP STATUS KEMISKINAN BALITA DI KEPULAUAN MALUKU DAN PULAU PAPUA. Seminar Nasional Official Statistics, 2020(1). https://doi.org/10.34123/semnasoffstat.v2020i1.569

Ananth, C. V., & Kleinbaum, D. G. (1997). Regression models for ordinal responses: A review of methods and applications. International Journal of Epidemiology, 26(6). https://doi.org/10.1093/ije/26.6.1323

Anisa, K. K. (Badan P. S. (2021). Kemiskinan Anak di Kalimantan Barat Tahun 2021 | Jurnal Forum Analisis Statistik (FORMASI). http://jurnal.bpskalbar.com/index.php/jsa/article/view/24

Ari, E., & Yildiz, Z. (2014). PARALLEL LINES ASSUMPTION IN ORDINAL LOGISTIC REGRESSION AND ANALYSIS APPROACHES. International Interdisciplinary Journal of Scientific Research, 1(3), 8–23. https://www.semanticscholar.org/paper/PARALLEL-LINES-ASSUMPTION-IN-ORDINAL-LOGISTIC-AND-Ari-Yildiz/b060cdb6ac7ed22d97371e717a539b400bf22464#citing-papers

Bernstein, S. F., Rehkopf, D., Tuljapurkar, S., & Horvitz, C. C. (2018). Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model. PLoS ONE, 13(5). https://doi.org/10.1371/journal.pone.0195734

BPS. (2013). Penghitungan dan Analisis Kemiskinan Makro Indonesia Tahun 2013.

BPS. (2021a). Penghitungan dan Analisis Kemiskinan Makro Indonesia Tahun 2021. https://www.bps.go.id/publication/2021/11/30/9c24f43365d1e41c8619dfe4/penghitungan-dan-analisis-kemiskinan-makro-indonesia-tahun-2021.html

BPS. (2021b, December). Angkringan Statistik (Antik) “Kemiskinan Ekstrem dalam Perspektif Antropologi Kependudukan.” https://yogyakarta.bps.go.id/news/2021/12/24/81/angkringan-statistik--antik---kemiskinan-ekstrem-dalam-perspektif-antropologi-kependudukan-.html

Budiantoro, S., Martha, L. F., & Sagala, M. (2015). Penghitungan Indeks Kemiskinan Multidimensi Indonesia 2012-2014. https://repository.theprakarsa.org/media/301093-indeks-kemiskinan-multidimensi-indonesia-f8c2448d.pdf

Busienei, P. J., Ogendi, G. M., & Mokua, M. A. (2019). Open Defecation Practices in Lodwar, Kenya: A Mixed-Methods Research. Environmental Health Insights, 13. https://doi.org/10.1177/1178630219828370

Cooper, R. N., & Sachs, J. D. (2005). The End of Poverty: Economic Possibilities for Our Time. Foreign Affairs, 84(3). https://doi.org/10.2307/20034362

Dalati, S. (2018). Measurement and Measurement Scales. Springer. https://doi.org/10.1007/978-3-319-74173-4_5

Dolgun, A., & Saracbasi, O. (2014). Assessing proportionality assumption in the adjacent category logistic regression model. Statistics and Its Interface, 7(2). https://doi.org/10.4310/SII.2014.v7.n2.a12

Fernandez, D., Liu, I., Costilla, R., Sant Joan de Déu, S., Sant Joan, F., & Pujadas, A. (2019). A method for ordinal outcomes: The ordered stereotype model. International Journal of Methods in Psychiatric Research, 28(4), e1801-1. https://doi.org/10.1002/MPR.1801

Harahap, M. R. A. (2017). Analisis Tingkat Kemiskinan Rumah Tangga Di Kota Padangsidimpuan. https://repositori.usu.ac.id/handle/123456789/920

Hastuti, H. . (2015). Peran Perempuan dalam Pengentasan Kemiskinan di Desa Wisata Gabugan, Sleman, Daerah Istimewa Yogyakarta. SOCIA: Jurnal Ilmu-Ilmu Sosial, 11(2). https://doi.org/10.21831/socia.v11i2.5300

Hasyim, M. N. A., & Veriyanto, A. (2022). ANALISIS DETERMINAN RUMAH TANGGA MISKIN DI PROVINSI KALIMANTAN UTARA TAHUN 2020. Jurnal Ekonomika, 13(01). https://doi.org/10.35334/jek.v13i01.2422

Jayanthi, R. (2021). The Effect of Electricity Development in Indonesia on Poverty and Income Inequality. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 22(1). https://doi.org/10.23917/jep.v22i1.12076

Khabhibi, A. (2013). Analisis faktor-faktor yang mempengaruhi tingkat kemiskinan. https://digilib.uns.ac.id/dokumen/detail/30480/Analisis-faktor-faktor-yang-mempengaruhi-tingkat-kemiskinan

Kwan, C., & Walsh, C. A. (2018). Old age poverty: A scoping review of the literature. In Cogent Social Sciences (Vol. 4, Issue 1). https://doi.org/10.1080/23311886.2018.1478479

Long, J. S., & Freese, J. (2001). Regression Models for Categorical Dependent Variables Using STATA. In Sociology The Journal Of The British Sociological Association: Vol. Revised ed. https://doi.org/10.1186/2051-3933-2-4

Long, J. S., & Freese, J. (2014). Stata Bookstore: Regression Models for Categorical Dependent Variables Using Stata, Third Edition. In Stata Press.

McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2). https://doi.org/10.1111/j.2517-6161.1980.tb01109.x

Merdekawati, I. P., & Budiantara, I. N. (2013). Pemodelan Regresi Spline Truncated Multivariabel pada Faktor-Faktor yang Mempengaruhi Kemiskinan di Kabupaten/Kota Provinsi Jawa Tengah. Jurnal Sains Dan Seni Pomits, 2(1).

Njuguna, J. (2019). Progress in sanitation among poor households in Kenya: Evidence from demographic and health surveys. In BMC Public Health (Vol. 19, Issue 1). https://doi.org/10.1186/s12889-019-6459-0

Noren Hooten, N., & Evans, M. K. (2019). Age and poverty status alter the coding and noncoding transcriptome. Aging, 11(4). https://doi.org/10.18632/aging.101823

Nugroho, S. S. (2015). THE ROLES OF BASIC INFRASTRUCTURE ON POVERTY ALLEVIATION IN INDONESIA. Kajian Ekonomi Dan Keuangan, 19(1).

Park, J. S. (2018, October 8). Top Factors that Lead to Poverty - The Borgen Project. https://borgenproject.org/top-factors-that-lead-to-poverty/

Sen, A. (2000). Development as Freedom. Alfred A. Knof Publishing.

Suryana, S., & Swarniati, K. (2021). ERADICATING POVERTY AND HUMAN CAPITAL DEVELOPMENT IN INDONESIA: An Approach with Multilevel Logistic Regression Model. WELFARE : Jurnal Ilmu Kesejahteraan Sosial, 10(2). https://doi.org/10.14421/WELFARE.2021.102-01

Syahrani, E., Kusumaningdyah, A. A., & Dewa, D. D. (2021). Analisis Faktor-Faktor yang Mempengaruhi Tingkat Kemiskinan Kabupaten/Kota di Jawa Tengah. Geodika: Jurnal Kajian Ilmu Dan Pendidikan Geografi, 5(2), 247–258. https://doi.org/10.29408/GEODIKA.V5I2.4033

Tutz, G. (2022). Ordinal regression: A review and a taxonomy of models. In Wiley Interdisciplinary Reviews: Computational Statistics (Vol. 14, Issue 2). https://doi.org/10.1002/wics.1545

Tutz, G., & Schauberger, G. (2013). Visualization of categorical response models: From data glyphs to parameter glyphs. Journal of Computational and Graphical Statistics, 22(1). https://doi.org/10.1080/10618600.2012.701379

UN. (2019). Income Poverty in Old Age: An Emerging Development Priority. https://www.un.org/esa/socdev/ageing/documents/PovertyIssuePaperAgeing.pdf

UNSD. (2005). United Nations Statistics Division. Handbook on Poverty Statistics: Concepts, Methods and Policy Use. https://unstats.un.org/unsd/methods/poverty/chapters.htm

Williams, R. A., & Quiroz, C. (2020). Ordinal Regression Models. SAGE Research Methods Foundations. https://doi.org/10.4135/9781526421036885901

Winship, C., & Mare, R. D. (1984). Regression Models with Ordinal Variables. American Sociological Review, 49(4). https://doi.org/10.2307/2095465

World Bank. (2022). Poverty Overview: Development news, research, data | World Bank. https://www.worldbank.org/en/topic/poverty/overview#1

Zuhri, K., Anggraeni, L., & Irawan, T. (2019). Inequality in Indonesia’s Electricity Access. International Journal of Sciences: Basic and Applied Research, 48(7).




DOI: https://doi.org/10.21831/socia.v19i2.52555

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