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Year-On-Year Change (%) In Average Monthly Nominal Earnings Per Employee, Quarterly

Ministry of Manpower (MOM) derived statistics track year-on-year percentage changes in average monthly nominal earnings per employee on a quarterly basis. Figures are compiled from Central Provident Fund (CPF) contribution records for employees with CPF contributions, as detailed in the official notes on AME definition and limitations. Labour economists use the series as a timely gauge of wage movement while heeding guidance that mean earnings can be skewed and mix-sensitive. Catalogue coverage spans 1999 through 2022; no member CSV is currently attached to the collection.

Official description

Source : Derived based on data from Central Provident Fund Board Notes : 1) Average (Mean) Monthly Earnings (AME) refers to all remuneration received before deduction of the employee Central Provident Fund (CPF) contributions and personal income tax. It comprises basic wages, overtime pay, commissions, allowances and bonuses but exclude employer CPF contributions. 2) Data cover full-time and part-time employees who have CPF contributions. They exclude identifiable self-employed persons who have made CPF contribution. 3) Average (Mean) Monthly Earnings covers employees who contribute to the Central Provident Fund. Available quarterly, AME provides a timely rough gauge of average wage movement. However, the AME can be influenced by the changing mix of full-time and part-time workers and variations in overtime as the data are compiled based on the payroll of contributors to the CPF which do not allow for breakdown separately to full-timers and part-timers. Also, as AME pertains to mean earnings, it can be skewed upwards by a small number of very high income earners. For income of a typical worker, users should refer to the median Gross Monthly Income From Work and for wage growth of employees over a year they can refer to the Annual Wage Changes, both of which are available on MOM’s website.

Published by
Ministry of Manpower
Update frequency
quarterly
Datasets
0

Datasets in this collection