Malaysia had approximately 2 million migrants in 2018, and this number was increasing dramatically by 25 percent in 2019. Parallels with the aims of country policy to reduce migrant workers' dependency in 2020, managing the workers needs to be clarified. At the same time, the country still needs to keep them for specific sectors. These issues motivate us to analyze the migrant worker's requirements at different levels of skills and wages. Using Computable General Equilibrium (CGE) modeling, at four-level nested CES production function, this study found high skilled migrants will harm wages for the high skilled and skilled groups while the opposite effect was observed for the semiskilled and low-skilled groups. However, when the migrant stock increases slightly below 1 percent, it will reduce the wages for semiskilled workers due to substitution effects. This study also found that the influx of low-skilled migrant workers will reduce salaries for semiskilled and low-skilled workers. The analysis also indicates that a small rise in high skilled immigrant labour will reduce the unemployment rate; likewise, increasing more than 4 percent will increase the unemployment rate. The results provide the policymaker guidelines to employ foreign workers' best skills to control the inequality of wages among skilled and low-skilled workers.
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.
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