The Impact of Automation Adoption on Employment Structures: A Longitudinal Panel Analysis of Skill-Biased Technological Change in Malaysia’s Manufacturing Sector, 2012-2023

Abstract

This paper examines the impact of automation adoption on employment structures within Malaysia’s manufacturing sector from 2012 to 2023, focusing on shifts in job roles, wage dynamics, skill demands, and union influence. Employing a multi-method analytical framework, the research integrates longitudinal data from diverse sources to uncover trends, correlations, and causal relationships. Descriptive analysis reveals a 15 percentage-point increase in high-skill employment, a corresponding decline in low-skill roles, widening wage disparities, rising gig work prevalence, and a marked reduction in union density. Correlation analysis demonstrates a strong positive relationship between automation and high-skill employment (r=0.98, p<0.01) as well as wages (r=0.96, p<0.01), alongside a strong negative correlation with low & semi-skill jobs (r=-0.97, p<0.01). Automation’s link to low & semi-skill wages is weak and statistically insignificant (r=0.35, p>0.05), while gig work prevalence shows a strong positive correlation (r=0.92, p<0.01). A strong negative correlation with union density (r=-0.89, p<0.01) suggests automation erodes collective bargaining power. Ordinary Least Squares (OLS) regression analysis further confirms that automation adoption significantly drives high-skill job creation (β₁=0.72, p<0.01) and exacerbates skill gaps (β₂=0.15, p<0.01), consistent with Skill-Biased Technological Change (SBTC) theory.  These findings underscore automation’s dual role in fostering productivity gains while intensifying labor market polarization and socio-economic inequalities. The study recommends modernizing education, expanding reskilling programs, and strengthening social safety nets to mitigate adverse effects.