OpenDOSM

Unemployment Rate (Annual)

Data as of 31 Dec 2021, 23:59

Ratio of unemployed to labour force size

How is this data produced?

The Labour Force Survey (LFS) provides statistics on the labour force, employment and unemployment at national and state level, as well as across urban and rural areas. Data from this survey is published on a monthly, quarterly and annual basis. A comprehensive and systematic approach in data collection and processing has been maintained over a period of time in order to obtain comparable time series statistics.

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

There is no data for 1991 and 1994 because the LFS was not conducted in those years. The sum of each category may not always equal to the totals shown in related tables because of independent rounding to one decimal place. However, the differences are not obvious.

Publication(s) using this data

Key Statistics of the Labour Force in Malaysia, November 2022, the latest edition of the monthly labour statistics published by DOSM.

Metadata

Dataset description

The annual Labour Force Report presents the statistics of labour force, employment and unemployment obtained from the Labour Force Survey (LFS).

Variable definitions
  • Unemployment Rate (Annual)
  • Date
  • Labour Force Size (Annual)
  • Employed Persons (Annual)
  • Unemployed Persons (Annual)
  • Persons Outside Labour Force (Annual)
  • Participation Rate (Annual)
  • Employment-Population Ratio (Annual)
Last updated

28 Feb 2022, 12:00

Next update

28 Feb 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/labour-principalstats-annual.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)