Resilience of Workers Affected by COVID-19 Outbreak in Maintaining Their Jobs, in Which Sector Survives Most Longer?

Authors

  • Fenanda Dwitha Kurniasari Statistics Indonesia
  • Yulia Atma Putri Statistics Indonesia

DOI:

https://doi.org/10.34123/icdsos.v2021i1.238

Keywords:

employment, survival analysis, covid-19

Abstract

Employment is one of the areas affected during the covid-19 outbreak. The government of Indonesia has taken numerous measures to restrain the growth rate of covid-19, such as the implementation of social restriction, which leads to a multidimensional problem – the employment problem. Indonesia’s unemployment in 2020 has increased compared to 2019. According to Statistics Indonesia, the open unemployment rate in August 2020 is about 1.84 percent higher than August 2019, and from the total working-age population in August 2020, 14.28 percent of them were affected by covid-19. This study aims to investigate the resilience of workers affected by the covid-19 outbreak in maintaining their jobs by comparing the survival rates in the sectors most affected by covid. The methodology used in this research is survival analysis in time resilience of workers affected by the covid-19 outbreak in maintaining their jobs. The conclusion obtained from this study is that the sector significantly influences worker’s time resilience (p-value < 0.05). Among the six sectors most affected by covid-19, workers in the construction sector has the highest time resilience compared to 5 other sectors – most survive workers in maintaining their jobs during covid-19 outbreak, followed by the accommodation and food services, other services activities, manufacturing, wholesale and retail trade sectors. The most affected sector for the time resilience of workers during the COVID-19 outbreak is transportation and storage.

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Published

2022-01-04

How to Cite

Kurniasari, F. D., & Putri, Y. A. (2022). Resilience of Workers Affected by COVID-19 Outbreak in Maintaining Their Jobs, in Which Sector Survives Most Longer? . Proceedings of The International Conference on Data Science and Official Statistics, 2021(1), 691–696. https://doi.org/10.34123/icdsos.v2021i1.238