Determinant of Labor Force Resilience Against The Employment Impact of The Covid-19 Pandemic in Bali Province, Indonesia: An Application of Survival Analysis

Ningsih, Ni Luh Putu Yayang Septia; Dokhi, Mohammad

doi:10.34123/icdsos.v2021i1.101

Cited by 2 publications

(1 citation statement)

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“…Meanwhile, 16.24% of other companies received more than a 75% revenue increase. The workforce in Bali Province at most stopped working in the first two months of the pandemic, namely March and April 2020 (Ningsih & Dokhi, 2022). The Large-Scale Social Restrictions (PSBB) policy in South Sulawesi reduced the number of working hours and even caused commuter workers not to work for a while (Triany, 2021).…”

Section: Mustofamentioning

confidence: 99%

Labor Income in DIY During The Covid-19 Pandemic

Mustofa

2022

IJSSR

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This article discusses workers' income in the Special Region of Yogyakarta (DIY) during the covid-19 pandemic. The data source used by Sakernas DIY for the August 2021 period with a selected sample of 4,029 workers. The analysis technique used is a regression analysis technique. The results showed that labour income during the Covid-19 period was influenced by education level, work experience, gender, marital status, job training, number of hours worked, digital devices, and work status, with a total effect of 40.5%. Workers with higher levels of education have better earnings. Workers who are equipped with job training have higher earnings. The number of hours affects income positively and significantly. Workers who use digital devices have higher incomes. Self-employment has a negative influence on income during a pandemic.

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Section: Mustofamentioning

confidence: 99%

Labor Income in DIY During The Covid-19 Pandemic

Mustofa

2022

IJSSR

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Recovery time of the hotel and restaurant sector in Indonesia after COVID-19 crisis: a survival analysis

Sastri,

Li,

Naveed

et al. 2024

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PurposeThe COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.Design/methodology/approachThis study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.FindingsThe empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.Originality/valueThis study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

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Determinant of Labor Force Resilience Against The Employment Impact of The Covid-19 Pandemic in Bali Province, Indonesia: An Application of Survival Analysis

Cited by 2 publications

References 1 publication

Labor Income in DIY During The Covid-19 Pandemic

Labor Income in DIY During The Covid-19 Pandemic

Recovery time of the hotel and restaurant sector in Indonesia after COVID-19 crisis: a survival analysis

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