The Nexus Between COVID-19 and Digital Gig Economy in Malaysia: An Empirical Investigation Using Vector Autoregression (VAR) Model

Abstract

This paper aims to investigate potential causal relationships between the digital gig economy, COVID-19, and unemployment in Malaysia. The initial part of the study consisted of determining whether the variables were stationary. The ADF findings indicated that all variables are stationary at the level and first difference. Because series are integrated with different orders, this study employs the Vector Autoregression (VAR) model to investigate the impact of the pandemic and unemployment on the digital gig economy. A variance decomposition or forecast error variance decomposition (FEVD) is employed as additional evidence presenting more detailed information regarding the variance relations between the selected variables. The evidence points to the fact that COVID-19 has a significant negative short-run impact on the digital gig economy. The Granger causality test shows a unidirectional relationship between COVID-19 and the gig economy. Variance decomposition results found that the digital gig economy is explained by itself in the short run, so other variables in the model do not strongly influence the variable. However, COVID-19 cases, death, and unemployment can strongly predict the gig economy. The results suggest that COVID-19 instances impact online occupations since a health crisis may harm the main factor of production, which is labour, thus directly impacting labour productivity, even when the task may be completed from home. Besides that, the study suggests that that Malaysian adaptability, which refers to a worker’s capacity to successfully manage psychosocial functions in response to new, changing, and unexpected events, settings, and situations, may be delayed.