Download fulltext HTML
Arkum, D., Budi Marwoto, P., & Ghaly Ghiffary, G. (2023). Analysis of Pre-post Covid-19 Influence in Bangka Belitung Islands Province: Socio-Economic Aspects in 7 Regencies/Cities. KnE Social Sciences, 8(12), 104–114. https://doi.org/10.18502/kss.v8i12.13655

https://doi.org/10.18502/kss.v8i12.13655

statistics

  • 116 Abstract Views
  • 68 PDF Views
  • 0 HTML Views

authors

  • Darol Arkum

    Darol Arkum

    Regional Development Planning Agency (Bappeda) of Bangka Regency, Indonesia
  • Pan Budi Marwoto

    Pan Budi Marwoto

    Regional Development Planning Agency (Bappeda) of Bangka Regency, Indonesia
  • Ghardapaty Ghaly Ghiffary

    Ghardapaty Ghaly Ghiffary

    Regional Development Planning Agency (Bappeda) of Bangka Regency, Indonesia

Published Date

  • Jul 18, 2023

Analysis of Pre-post Covid-19 Influence in Bangka Belitung Islands Province: Socio-Economic Aspects in 7 Regencies/Cities

KnE Social Sciences / Transdisciplinary Symposium on Business, Economics, and Communication (TSBEC 2022) / Pages 104–114

Abstract

This study will analyze the variables of economic growth, poverty rate, unemployment rate, and the Human Development Index using situational trend analysis of the 4 factors before - after Covid-19 and spatial autocorrelation to see the inter-regional linkages as well as the distribution pattern of the observed data. The purpose of the study was: 1) to find out the description of the situation of the 4 factors before – after Covid-19; and 2) knowing the spatial autocorrelation based on Moran’s index with the spatial weighting matrix (WIJ). Based on the results of the analysis, the rate of economic growth in each district/city of Prov. Bangka Belitung tends to have a downward trend in the period 2018–2020 except Kab. Bangka and Pangkalpinang City. The variable rate of economic growth also did not have a spatial autocorrelation during the 2018-2021 period, which was indicated by the distribution pattern of the 2018-2019 data spreading (the Moran index was negative) and 2020-2021 was clustered (the Moran index was positive). Unemployment variable in all districts/cities of Prov. Bangka Belitung had a significant upward trend in the 2018-2021 period. The variable percentage of unemployment also did not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 time period. The poverty variable in all districts/cities of Prov. Bangka Belitung had a downward trend in the 2018-2021 period. The variable percentage of poverty also does not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 observation period (the Moran index was negative). Variable Human Development Index in all regencies/cities prov. Bangka Belitung has an upward trend during the 2018-2021 timeframe. The HDI variable also does not have a spatial autocorrelation, but the data distribution pattern tends to collect during the 2018-2021 time period (positive Moran index).


Keywords: economic growth, poverty, unemployment, human development, spatial autocorrelation, Covid 19

References
[1] Saputra DN, Valeriani D, Ningrum C. Pengaruh infrastruktur ekonomi dan infrastruktur sosial terhadap pertumbuhan ekonomi Provinsi Kepulauan Bangka Belitung. KLASSEN. 2021;1(2):111-123.

[2] Romi S, Umiyati E. Pengaruh pertumbuhan ekonomi dan upah minimum terhadap kemiskinan di Kota Jambi. E-Jurnal Perspektif Ekonomi Dan Pembangunan Daerah. 2018;7(1):1-7.

[3] Basuki AT, Mulyanto. Causes of economic growth in Indonesia: Evidence from eighteen provinces. International Journal of Social and Economic Research. 2017;3772-3789.

[4] Suharlina H. Pengaruh Investasi, Pengangguran, Pendidikan dan Pertumbuhan Ekonomi Terhadap Kemiskinan Serta Hubungannya dengan Kesejahteraan Masyarakat Kabupaten/Kota di Provinsi Kalimantan Barat. Prosiding Seminar Akademik Tahunan Ilmu Ekonomi dan Studi Pembangunan. 2020;56-72.

[5] Palindangan J, Bakar A. Analisis Pengaruh Tingkat Pertumbuhan Ekonomi dan Indeks Pembangunan Manusia (IPM) Terhadap Tingkat Pengangguran di Kabupaten Mimika. JURNAL KRITIS (Kebijakan, Riset, Dan Inovasi). 2021;5(1): 65-80.

[6] Prasetyoningrum AK, Sukmawati US. Analisis Pengaruh Indeks Pembangunan Manusia (IPM), Pertumbuhan Ekonomi dan Pengangguran Terhadap Kemiskinan di Indonesia. Equilibrium: Jurnal Ekonomi Syariah. 2018;6(2):217-240.

[7] Maulana R, Pitoyo AJ, Alfana MAF. Analisis Pengaruh Kemiskinan dan Kondisi Ekonomi Terhadap Indeks Pembangunan Manusia di Provinsi Jawa Tengah Tahun 2013-2017. Media Komunikasi Geografi. 2022;23(1):12-24.

[8] Walpole. Pengantar Statistika. Jakarta: Gramedia Pustaka Utama; 1995.

[9] Ismail S. Spatial autocorrelation and real estate studies: A literature review. Malaysian Journal of Real Estate. 2006;1(1):1-13.

[10] Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Wilson R. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography. 2007:609-628.

[11] Cliff A, Ord J. Spatial autocorrelation. 1973.

[12] Gittleman JL, Kot M. Adaptation: Statistics and a null model for estimating phylogenetic effects. Systematic Zoology. 1990;39(3):227-241.

[13] Lutfi A, Adid MK, Sudarmin. Identifikasi Autokorelasi Spasial Angka Partisipasi Sekolah di Provinsi Sulawesi Selatan Menggunakan Indeks Moran. VARIANSI: Journal of Statistics and Its Application on Teaching and Research. 2019;1.

[14] Lee J, Wong DW. Statistical analysis with ArcView GIS. John Wiley & Sons; 2001.

[15] Zhukov Y. Spatial autocorrelation. IQQS, Harvard University, Amerika; 2010.