Unveiling Demographic Dynamics: A Multinomial Logistic Analysis of Employment Disparities Among Malaysia's Ageing Population

Abstract

Anticipated to transition into an ageing society by 2030, Malaysia faces a pressing challenge related to workforce dynamics. Although demographic factors play a pivotal role in shaping the employment status of the elderly, existing analyses remain insufficient in providing comprehensive insights. To address this gap, this study employs a multinomial logistic regression model to examine disparities in the employment status of older individuals based on demographic factors. The study identifies five distinct categories of employment status among the elderly: employers, employees, self-employed individuals, unpaid family workers, and those not engaged in employment. Using microdata from the Labour Force Survey conducted by the Department of Statistics Malaysia between 2019 and 2021, the analysis focuses on a carefully selected sample of individuals age 60 and above: 46,315 people in 2019, 43,419 people in 2020, and 45,151 people in 2021. The demographic factors examined include strata, educational attainment, marital status, geographic zone, and ethnic group. This comprehensive approach seeks to highlight inequality gaps in elderly employment status across these demographic factors. The findings underscore the significance of the multinomial logistic regression model in understanding employment disparities among the elderly. Notably, lack of formal education, low education level, and marital status emerge as significant determinants. In essence, this study sheds light on the complexities of Malaysia's demographic landscape, offering valuable insights into the challenges faced by its ageing population and highlighting key factors shaping the employment trajectory of older adults.