Main Article Content
Abstract
This paper intends to observe the economic impact and spending patterns of citizens when the spread of coronavirus occurred uncontrollably in Malaysia. The data used in this paper is the result of a first-round Special Survey ‘Effect of COVID-19 on Economy and Individual’ conducted by the Department of Statistics Malaysia. Based on the paired-t test, all the aspects tested are found to have significant differences before and during the pandemic. This is likely because citizens comply with the rules set during Movement Control Order. Next, the Chi-square test between the changes in citizens’ monthly income and sociodemographic factors are significantly associated. Therefore, factors state, gender, ethnicity, and age group of citizens are used in further analysis of multiple correspondence (MCA) to study the relationship between several categorical variables. Hence, citizens of age group 35–44 years old from Central region and Chinese citizens have no changes in their income before and during the pandemic. Citizens of age group 15–34 years old and Northern region have both increment or reduction income whereas men citizens received lower-income payments. Indian women and citizens from Eastern region tend to not work during the pandemic. This study can be a guide to the government in overcoming social problems.
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References
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References
Y.C. Wu, C.S. Chen, and Y.J. Chan, “The Outbreak of COVID-19: An Overview,” Journal of Chinese Medical Association 83 (3), 217 (2020).
Unknown. “Symptoms of COVID-19,” https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. Retrieved 7 January 2022.
World Health Organization. “WHO Director-General's opening remarks at the media briefing on COVID-19¬–11 March 2020,” https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 Retrieved 7 January 2022.
A. Tang, “Malaysia Announces Movement Control Order After Spike in COVID-19 Cases (Updated),” https://www.thestar.com.my/news/nation/2020/03/16/malaysia-announces-restricted-movement-measure-after-spike-in-covid-19-cases. Retrieved 15 October, 2021.
I.S.Z Abidin, “Analisis Ekonomi Bukti Negara Tidak Gagal Urus Impak COVID-19,” https://www.bharian.com.my/kolumnis/2021/07/841952/analisis-ekonomi-bukti-negara-tidak-gagal-urus-impak-covid-19. Retrieved 15 October 2021.
A. Ahmad et al., “Changes in Health-Related Lifestyles and Food Insecurity and Its Association with Quality of Life during the COVID-19 Lockdown in Malaysia.” BMC Public Health, 22(1), 1-9 (2022).
Unknown. “COVID-19 Pandemic: Frequently Asked Questions on Employment,” https://www.skrine.com/insights/alerts/april-2020/covid-19-pandemic-frequently-asked-questions-o-1. Retrieved 13 January 2022.
R. Raihan, “A Complete Guide to COVID-19 Financial Assistance In Malaysia,” https://www.homage.com.my/resources/covid-19-financial-assistance-malaysia/. Retrieved 14 January 2022.
A.A. Azlan, M.R. Hamzah, T.J. Sern, S.H. Ayub, and E. Mohamad. “Public Knowledge, Attitudes and Practices towards COVID-19: A Cross-Sectional Study in Malaysia,” Plos one 15(5), e0233668 (2020).
S. Jha, and V. Pradhan, “Factors causing change in customer behaviour during COVID-19 Pandemic,” Journal of Critical Reviews 7(19), 3792-3804 (2020).
N. Topaç, M. Bardak, S.B. Kalkan, and M. Kirisci. “Early Childhood Children in COVID-19 Quarantine Days and Multiple Correspondence Analysis,” Journal of Mathematical Sciences and Modelling 3(3): 130-134 (2020).
O. Nenadic and M. Greenacre, “Correspondence Analysis in R, with Two-and Three-Dimensional Graphics: the ca Package,” Journal of Statistical Software, 20: 1-13 (2007).
A. Crossman, “Pembersih Data,” https://ms.eferrit.com/pembersihan-data/#. Retrieved 12 November 2021.
K.F. Hidayati. Tak Hanya Agar Rapi, Pahami Arti dan Pentingnya Data Cleansing, Yuk!. Glints. https://glints.com/id/lowongan/data-cleansing-cleaning/#.YrCOz0ZBzIX. Retrieved 22 November 2021.
T.K. Kim, “T Test as a Parametric Statistic,” Korean Journal of Anesthesiology, 68(6), 540-546 (2015).
M.L. McHugh, “The chi-square test of independence,” Biochemia Medica, 23(2), 143-149 (2013).
Greenacre, M., Correspondence Analysis in Practice, edition. Boca Raton, Chapman dan Hall/CRC, 2007.
L. Lebart, A. Morineau, and K.M. Warwick, Multivariate Descriptive Statistical Analysis; Correspondence Analysis and Related Techniques for Large Matrices. New York US, John Wiley & Sons, Inc. (1984).
O. Nenadic and M. Greenacre, Computation of Multiple Correspondence Analysis, with code in R. Barcelona, Universtat Pompeu Fabra, 2012.
M. Greenacre, Biplots in Practice. Bilbao dan Madrid, Fundación BBVA, 2010.
A. Kassambara, Articles–Principal component methods in R: practical guide, HCPC–Hierarchical Clustering on Principal Components: Essentials. STHDA (Statistical tools for highthroughput data analysis) (2017a), 2018.