Existence of asymmetry between wages and automatable jobs: a quantile regression approach

Purpose - This study aims to explore, in the context of Machinery and Equipment sector of Malaysia, the association between average wages and share of employment in automatable jobs, specifically whether the association between average wages and share of employment automatable jobs is asymmetric in nature. Design/methodology/approach - The responses obtained from the structured interview of 265 firms are used to build up the empirical models (conditional mean regression and quantile regression). Findings - The conditional mean regression findings show that employment levels in some low-waged, middle-skilled jobs are negatively associated with average wages. Furthermore, the quantile regression results add that firms that possess higher levels of share of employment in automation jobs are found to have a stronger association to average wages than those possessing a lower share of employment in automation jobs. Practical implications - From the theoretical perspective, the findings of this study add to the body of knowledge of the theory of minimum wages and the concept of job polarization. From a policy perspective, the findings of this study can serve as a critical input to standard setters and regulators in devising industrial and as education policies. Originality/value - Based on the assumption of a constant average policy effect on automatable jobs, conditional mean regression models have been commonly used in prior studies. This study makes the first attempt to employ the quantile regression method to provide a deeper understanding of the relationship between wages and employment in automatable jobs.