ANALISIS DETERMINAN KEMISKINAN INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN MULTIPLE CLASSIFICATION ANALYSIS (MCA) - Natalia, Christiayu; Putranto, FX Gugus Febri (Jurnal Kajian Ekonomi dan Kebijakan Publik (JEpa); Vol 7 No 2 (2022): Jurnal Kajian Ekonomi dan Kebijakan Publik (JEpa), 2022)
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.