Annual Principal Labour Force Statistics by DUN
Data as of 2023Annual principal labour force statistics at DUN constituency level, including unemployment and participation rates.
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How is this data produced?
This data is produced based on the Labour Force Survey (LFS), which is designed to collect representative data on the labour force at national, state, and administrative district level. The raw survey data was subsequently aggregated at the DUN constituency level. Consistency in methodology has been maintained to ensure that the data is comparable over time. Survey findings are a monthly, quarterly and annual basis. For a full description of the methodology, please refer to the Technical Notes.
What caveats I should bear in mind when using this data?
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.
Metadata
Dataset description
Annual principal labour force statistics at DUN constituency level, including unemployment and participation rates.
Variable definitions
- State
- Parliamentary Constituency
- DUN Constituency
- Date
- Labour Force Size
- Employed Persons
- Unemployed Persons
- Persons outside Labour Force
- Unemployment Rate
- Participation Rate
- Employment-Population Ratio
Name in Dataset | Variable | Definition |
|---|---|---|
state (Categorical) | State | One of 13 states with a state legislative assembly; the 3 Federal Territories are excluded as they do not have State Legislative Assemblies. |
parlimen (Categorical) | Parliamentary Constituency | One of 207 parliamentary constituencies; the 15 constituencies in Federal Territories are excluded as they are not subdivided into DUNs. |
dun (Categorical) | DUN Constituency | One of 600 DUN constituencies |
date (Date) | Date | The date in YYYY-MM-DD format, with MM-DD set to 01-01 since the data is at annual frequency |
lf (Float, '000) | Labour Force Size | The number (in thousands) of employed and unemployed individuals. This figure also represents the number of people participating in the labour force. |
lf_employed (Float, '000) | Employed Persons | The number (in thousands) of people who worked at least one hour for pay, profit or family gain, in thousands of people |
lf_unemployed (Float, '000) | Unemployed Persons | The number (in thousands) of people who did not work but were looking for work or available to work |
lf_outside (Float, '000) | Persons outside Labour Force | The number (in thousands) of people not classified as employed or unemployed, including housewives, students, early retired, disabled persons and those not interested in looking for a job |
u_rate (Float) | Unemployment Rate | Ratio of unemployed to labour force size |
p_rate (Float) | Participation Rate | Ratio of the labour force size to the working-age (15-64) population |
ep_ratio (Float) | Employment-Population Ratio | Ratio of the number of employed people to the working-age (15-64) population |
Last updated:
31 Dec 2025, 12:00
Next update:
31 Dec 2026, 12:00
Data source(s)
- Department of Statistics Malaysia
URLs to dataset
License
This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A copy of the license is available Here.
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Data
Full Dataset (CSV)
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Full Dataset (Parquet)
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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.dosm.gov.my/labour/lfs_dun.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)Sample OpenAPI query
The following code is an example of how to make an API query to retrieve the data catalogue mentioned above. You can use different programming languages by switching the code accordingly. For a complete guide on possible query parameters and syntax, please refer to the official Open API Documentation.
import requests
import pprint
url = "https://api.data.gov.my/data-catalogue?id=lfs_dun&limit=3"
response_json = requests.get(url=url).json()
pprint.pprint(response_json)
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