OpenDOSM

Cross section: Mean Wage by Age

Data as of 31 Dec 2021, 23:59

Mean wage in RM

How is this data produced?

The Salaries & Wages Survey collects data via a stratified two-stage sample design, thus obtaining representative data at the national and state level. The implementation of this survey is based on guidelines and recommendations of the International Labour Organization (ILO) with reference to An Integrated System of Wages Statistics. For more details on the survey design and scope, please refer to the Technical Notes in the latest Salaries & Wages Survey Report.

What caveats I should bear in mind when using this data?

The primary aim of the survey is to produce statistics on monthly wage and salary recipients. Accordingly, the data does not cover all individuals classified as 'employed' in the Labour Force Survey - the primary categories excluded are employers, own-account workers, and unpaid family workers. Furthermore, except for the data broken down by ethnicity, all data is for Malaysian citizens only.

Publication(s) using this data

Salaries & Wages Survey Report, 2021, the latest edition of the annual salaries and wages data published by DOSM.

Metadata

Dataset description

This dataset presents statistics from the annual Salaries & Wages Report. The implementation of the survey is based on guidelines and recommendations of the International Labour Organization (ILO) with reference to An Integrated System of Wages Statistics.

Variable definitions
Last updated

04 Oct 2022, 12:00

Next update

30 Aug 2023, 12:00

Data source(s)
  • DOSM
License

This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A human-readable copy of the license is available Here.

Download

Code

Connect directly to the data with Python.

# If not already installed, do: pip install pandas fastparquet import pandas as pd URL_DATA = 'https://storage.googleapis.com/dosm-public-economy/salaries_age_sex_xs.parquet' df = pd.read_parquet(URL_DATA) if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) print(df)

Department of Statistics Malaysia

© 2023 Department of Statistics Malaysia (DOSM)