ANALISIS DETERMINAN KEMISKINAN INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN MULTIPLE CLASSIFICATION ANALYSIS (MCA)

  • Christiayu Natalia Badan Pusat Statistik Kota Malang
  • FX Gugus Febri Putranto Badan Pusat Statistik Kota Batu

Abstract

Abstract: The percentage of poor people in Indonesia has increased during the COVID-19 pandemic. This study aims to provide an overview of the percentage of poor people in each province in Indonesia before and during the covid-19 pandemic, to find out and compare the variables that affect the percentage of poor people before and during the covid-19 pandemic. The data analyzed in this study were sourced from the Statistics Indonesia in 2019 and 2020. Descriptive analysis in graphs and tables was used to see an overview of the percentage of poor people in each province in Indonesia before and during the COVID-19 pandemic. Inferential analysis using Multiple Classification Analysis (MCA), is used to analyze the variables that affect the percentage of poor people in Indonesia before and during the COVID-19 pandemic. The variables analyzed in this study include the Unemployment Rate, Human Development Index, Morbidity Rate, and Percentage of Workers in the Informal Sector. The results of this study show that in general, provinces in Indonesia experienced an increase in the percentage of poor people during the COVID-19 pandemic. Multiple Classification Analysis (MCA) before the pandemic showed that Unemployment Rate, Human Development Index, Morbidity Rate and Percentage of Informal Sector Workers each significantly affected the percentage of poor people. During a pandemic, only Human Development Index and morbidity rates affect the percentage of the poor. Prior to the COVID-19 pandemic, the percentage of workers in the informal sector had the biggest impact on the percentage of the poor. However, during the COVID-19 pandemic, Human Development Index had the biggest influence. Based on the results of this study, it can be concluded that there are differences in variables that significantly affect the percentage of the poor before and during the CoVid-19 pandemic.

References

Bhowmik, D. (2021). Covid-19: Recession, Poverty and Inequality and Redistribution. International Journal on Recent Trends in Business and Tourism, 5(1), 11–21. https://doi.org/10.31674/ijrtbt.2021.v05i01.003
BPS. (2021a). Berita Resmi Statistik - Kemiskinan September 2020. https://www.bps.go.id/pressrelease/2021/02/15/1851/persentase-penduduk-miskin-september-2020-naik-menjadi-10-19-persen.html
BPS. (2021b). IPM. Sistem Rujukan Statistik. https://sirusa.bps.go.id/sirusa/index.php/indikator/46
BPS. (2021c). Kemiskinan. Sistem Rujukan Statistik. https://sirusa.bps.go.id/sirusa/index.php/variabel/1408
Dinata, S. R., Romus, M., & Yanti. (2020). Faktor Faktor yang Memengaruhi Tingkat Kemiskinan di Provinsi Riau Tahun 2003-2018. Al-Iqtishad, 2(16), 116–137.
Fadila, R., & Marwan, M. (2020). Pengaruh Indeks Pembangunan Manusia (IPM) dan Pertumbuhan Ekonomi terhadap Tingkat Kemiskinan di Provinsi Sumatera Barat periode tahun 2013-2018. Jurnal Ecogen, 3(1), 120. https://doi.org/10.24036/jmpe.v3i1.8531
Gammarano, R. (2019). The working poor or how a job is no guarantee of decent living conditions. April, 1–11. https://ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_696387.pdf
Ginting, A. L. (2020). Dampak Angka Harapan Hidup dan Kesempatan Kerja Terhadap Kemiskinan. EcceS (Economics, Social, and Development Studies), 7(1), 42. https://doi.org/10.24252/ecc.v7i1.13197
Iqraam, M., & Sudibia, I. K. (2019). Pengaruh PDRB, Pendidikan, kesempatan Kerja, dan Persentase Penduduk Sektor Informal Terhadap Kemiskinan di Provinsi Bali. E-Jurnal EP Unud, 6(9), 1200–1229.
Jacobus, E. H., Kindangen, P., & Walewangko, E. (2018). Analisis Faktor-Faktor yang Memengaruhi Kemiskinan Rumah Tangga di Sulawesi Utara. Jurnal Pembangunan Ekonomi Dan Keuangan Daerah, 19(5), 1–18. https://ejournal.unsrat.ac.id/index.php/jpekd/article/view/19789
Maharani, V., Ramadhanty, A. P., Putra, G. M., Pratama, I. M., & Yuhan, R. J. (2020). Penentuan Faktor-Faktor yang Memengaruhi Tingkat Fertilitas Di Indonesia Tahun 2017 Dengan Metode Multiple Classification Analysis (Analisis Data SDKI 2017). Business Economic, Communication, and Social Sciences (BECOSS) Journal, 2(3), 241–249. https://doi.org/10.21512/becossjournal.v2i3.6478
Mishra, V., Seyedzenouzi, G., Almohtadi, A., Chowdhury, T., Khashkhusha, A., Axiaq, A., Wong, W. Y. E., & Harky, A. (2021). Health inequalities during COVID-19 and their effects on morbidity and mortality. Journal of Healthcare Leadership, 13, 19–26. https://doi.org/10.2147/JHL.S270175
Nurfitri Imro’ah, A. F. S. M. (2019). Penentuan Garis Kemiskinan Provinsi Menggunakan Metode Multiple Classification Analysis. Bimaster : Buletin Ilmiah Matematika, Statistika Dan Terapannya, 8(4), 789–798. https://doi.org/10.26418/bbimst.v8i4.36198
Puspita, D. W. (2015). Analisis Determinan Kemiskinan Di Provinsi Jawa Tengah. Jejak, 8(1), 100–107. https://doi.org/10.15294/jejak.v8i1.3858
Putri, A., Azzahra, A., Andiany, D. D., Abdurohman, D., Sinaga, P. P., & Yuhan, R. J. (2021). Perbandingan Faktor-Faktor yang Memengaruhi Tingkat Pengangguran Terbuka di Indonesia Sebelum dan Saat Pandemi Covid-19. Jurnal Kajian Ekonomi Dan Pembangunan, 3(2), 25–46.
Ramdani, M. (2015). Determinan Kemiskinan di Indonesia Tahun 1982-2012. Economics Development Analysis Journal, 4(1), 58–64. http://journal.unnes.ac.id/sju/index.php/edaj
Sari, N. K. W. P., & Kartika, I. N. (2020). FAKTOR YANG MEMENGARUHI PENDAPATAN PENDUDUK MISKIN DI KAWASAN BALI TIMUR PROVINSI BALI. E-Jurnal EP Unud, 9(4), 907–934.
Sihaloho, E. D., Kamilah, F. Z., Rahma, G. R., & ... (2020). Pengaruh Angka Tuberkulosis Terhadap Angka Kemiskinan Di Indonesia: Studi Kasus 407 Kabupaten Kota. Jurnal Ilmu Ekonomi Dan …, 20(2), 123–132. https://jurnal.uns.ac.id/jiep/article/view/42853
Stats, U. (2020). SDG’s Report of Poverty. https://www.un.org/sustainabledevelopment/wp-content/uploads/2019/07/E_Infographic_01.pdf
Stats, U. (2021). End poverty in all its forms everywhere Report. https://unstats.un.org/sdgs/report/2021/goal-01/
Sugiarto. (2018). Multiple Classification Analysis ( Mca ) Sebagai Metode Alternatif. Statistika, 6(2), 4.
Suseł, A. (2011). Multiple classification analysis. Theory and application to Demography. Acta Universitatis Lodziensis. Folia Oeconomica, 255.
Wijayanti, D. (2003). Analisis Konsentrasi Kemiskinan Di Indonesia Periode Tahun 1999-2003. 215–225.
Wirawan, I. M. T., & Arka, S. (2013). Analisis Pengaruh Pendidikan, PDRB Per Kapita dan Tingkat Pengangguran Terhadap Jumlah Penduduk Miskin Provinsi Bali. E-Jurnal EP Unud, 4(5), 546–560.
World Bank. (2021). Indonesia Overview. https://www.worldbank.org/en/country/indonesia/overview
Zuhdiyaty, N., & Kaluge, D. (2018). Analisis Faktor - Faktor Yang Memengaruhi Kemiskinan Di Indonesia Selama Lima Tahun Terakhir. Jurnal Ilmiah Bisnis Dan Ekonomi Asia, 11(2), 27–31. https://doi.org/10.32812/jibeka.v11i2.42
Published
2022-07-09
How to Cite
NATALIA, Christiayu; PUTRANTO, FX Gugus Febri. ANALISIS DETERMINAN KEMISKINAN INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN MULTIPLE CLASSIFICATION ANALYSIS (MCA). Jurnal Kajian Ekonomi dan Kebijakan Publik (JEpa), [S.l.], v. 7, n. 2, p. 254-266, july 2022. ISSN 2527-2772. Available at: <https://jurnal.pancabudi.ac.id/index.php/jepa/article/view/4206>. Date accessed: 29 mar. 2024.