OpenDOSM
Mean Income by Percentile and State
Data as of 31 Dec 2019, 23:59Mean monthly household income among households in percentile K, where K is an integer between 1 and 100.
How is this data produced?
This data is is based on the Household Income and Basic Amenities Survey (HIS & BA) 2019. The survey is carried out twice in five years to produce representative data regarding income, poverty and access to basic amenities for Malaysian households. In the original HIS & BA 2019 Report, this data was reported as deciles, specifically B1-B4 (also known as the B40), M1-M4 (M40) and T1-T2 (T20). This has now been deepened from deciles to percentiles, such that there are 100 datapoints available for each series.
What caveats I should bear in mind when using this data?
This data is more detailed than the originally-published statistics, and should therefore be interpreted with the appropriate level of caution.
Publication(s) using this data
Metadata
Dataset description
This data is is based on the Household Income and Basic Amenities Survey (HIS & BA) 2019. The survey is carried out twice in five years to produce representative data regarding income, poverty and access to basic amenities for Malaysian households. In the original HIS & BA 2019 Report, this data was reported as deciles, specifically B1-B4 (also known as the B40), M1-M4 (M40) and T1-T2 (T20). This has now been deepened from deciles to percentiles, such that there are 100 datapoints available for each series.
Variable definitions
Last updated
23 Feb 2023, 12:00
Next update
N/A
Data source(s)
- DOSM
URLs to dataset
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/hiesba_percentiles_new.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)