16 datasets found
  1. d

    Mean Monthly Household Gross Income of T20, M40 and B40 of households by...

    • archive.data.gov.my
    Updated Jul 28, 2020
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    (2020). Mean Monthly Household Gross Income of T20, M40 and B40 of households by strata, Malaysia - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/mean-monthly-household-gross-income-of-t20-m40-and-b40-of-households-by-strata-malaysia
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    Dataset updated
    Jul 28, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    This dataset shows the number of Mean Monthly Household Gross Income of top 20%, middle 40% and bottom 40% of households by strata, 2002 - 2019, Malaysia. Source : DEPARTMENT OF STATISTICS MALAYSIA

  2. Number of households in Malaysia 2020, by income group

    • statista.com
    Updated Oct 11, 2023
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    Statista (2023). Number of households in Malaysia 2020, by income group [Dataset]. https://www.statista.com/statistics/1375147/malaysia-number-of-households-by-income-group/
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    Dataset updated
    Oct 11, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Malaysia
    Description

    In Malaysia, the income groups are divided into bottom 40 percent, middle 40 percent, and top 20 percent. In 2020, there were more than **** million Malaysian households in each of the B40 and M40 income groups, while **** million belonged to the T20 income group.

  3. d

    Exploring Mental Health and Other Risk Factors of Obesity Among the...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 13, 2023
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    So, Robert (2023). Exploring Mental Health and Other Risk Factors of Obesity Among the Urban-Poor Community (B40) in Malaysia During COVID-19 Pandemic [Dataset]. http://doi.org/10.7910/DVN/BGDDPC
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    Dataset updated
    Nov 13, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    So, Robert
    Description

    A cross sectional study among residents in an urban-poor community in Kuala Lumpur, Malaysia to explore probable risk factors of obesity.

  4. f

    Fuel Subsidy Restructure Effectiveness

    • figshare.com
    xlsx
    Updated Mar 1, 2023
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    Yuen Yee Yen (2023). Fuel Subsidy Restructure Effectiveness [Dataset]. http://doi.org/10.6084/m9.figshare.22144838.v1
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    xlsxAvailable download formats
    Dataset updated
    Mar 1, 2023
    Dataset provided by
    figshare
    Authors
    Yuen Yee Yen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fuel Subsidy Restructure for B40 and M40 in Malaysia

  5. Interview guide for proactive measures to eradicate poverty in Malaysia in...

    • figshare.com
    pdf
    Updated Jun 1, 2023
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    DARSHANA DARMALINGGAM (2023). Interview guide for proactive measures to eradicate poverty in Malaysia in IR4.0 Era: A shared prosperity vision [Dataset]. http://doi.org/10.6084/m9.figshare.16735426.v3
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    DARSHANA DARMALINGGAM
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    This interview guide was used for the semi-structured online interviews conducted for the study

  6. d

    PPR Sri Pantai (B40 Community) Obesity, Mental Health and Quality of Life...

    • search.dataone.org
    Updated Nov 12, 2023
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    Neoh, Beng Sheng (2023). PPR Sri Pantai (B40 Community) Obesity, Mental Health and Quality of Life Malaysia [Dataset]. http://doi.org/10.7910/DVN/BKAVFR
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    Dataset updated
    Nov 12, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Neoh, Beng Sheng
    Description

    Exploring prevalence and risk factors of Obesity in the B40 Community

  7. Household Income, Expenditure and Basic Amenities Survey 2019 - Malaysia

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Jan 16, 2021
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    Department of Statistics Malaysia (2021). Household Income, Expenditure and Basic Amenities Survey 2019 - Malaysia [Dataset]. https://datacatalog.ihsn.org/catalog/8597
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    Dataset updated
    Jan 16, 2021
    Dataset authored and provided by
    Department of Statistics Malaysia
    Time period covered
    2019
    Area covered
    Malaysia
    Description

    Abstract

    The main objectives of the Household Income, Expenditure and Basic Amenities Survey 2019 are : - to gather information on the income distribution and consumption patterns of the household - to identify target groups such as B40 and poor households - to measure accessibility of basic amenities enjoyed by the household - to use expenditure data as an inputs for Consumer Price Index weight

    Geographic coverage

    All districts in Malaysia, rural and urban areas

    Analysis unit

    Household

    Universe

    Coverage of the survey are households living in private living quarters only.

    Kind of data

    Sample survey data [ssd]

    Mode of data collection

    Face-to-face [f2f]

  8. f

    Interview transcript with multi-stakeholder on training in Malaysia for...

    • figshare.com
    pdf
    Updated Aug 23, 2021
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    DARSHANA DARMALINGGAM (2021). Interview transcript with multi-stakeholder on training in Malaysia for poverty eradication [Dataset]. http://doi.org/10.6084/m9.figshare.16384770.v2
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    pdfAvailable download formats
    Dataset updated
    Aug 23, 2021
    Dataset provided by
    figshare
    Authors
    DARSHANA DARMALINGGAM
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    This file consists transcription of interview recordings conducted with Malaysia's multi-stakeholder group including policy makers, trainers, and trainees to gauge proactive measures to eradicate poverty in Malaysia via training.

  9. Mean monthly income per household Malaysia 2022, by ethnic group

    • statista.com
    Updated Nov 1, 2023
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    Statista (2023). Mean monthly income per household Malaysia 2022, by ethnic group [Dataset]. https://www.statista.com/statistics/856659/malaysia-average-monthly-household-income-by-ethnic-group/
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    Dataset updated
    Nov 1, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    Malaysia
    Description

    In 2022, ethnic Chinese households had the highest mean monthly household income in Malaysia, at around 10.66 thousand Malaysian ringgit. This was more than three thousand ringgit higher than Bumiputera households. Despite the implementation of affirmative action through Article 153 of the Malaysian constitution, the economic position of the Bumiputera vis-à-vis other ethnicities still left much room for improvement.

    Historical policies, ethnicity, and the urban-rural divide The Bumiputera make up the majority of the Malaysian population, yet have one of the lowest average monthly household incomes in Malaysia. This economic disparity could be explained by the effects of colonial policies that kept the Bumiputera largely in the countryside. This resulted in an urban-rural divide that was characterized by ethnicity, with the immigrant Chinese and Indian laborers concentrated in the urban centers, a demographic pattern that is still evident today.

    There was a considerable difference in urban and rural household incomes in Malaysia, with urban household income being around 3.6 thousand ringgit more than rural households. This was largely due to the fact that wages in urban areas had to keep up with the higher cost of living there. This thus impacted the average monthly incomes of the largely rural-based Bumiputera and the largely urban-based ethnic Chinese. This visible wealth inequality has led to racial tensions in Malaysia, and it is still one of the problem in the country amidst a new government led by Prime Minister Anwar Ibrahim, who was elected in 2022.

  10. f

    Descriptive statistics for B40 clustering model.

    • plos.figshare.com
    xls
    Updated Jun 10, 2023
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    Mariah Abdul Rahman; Nor Samsiah Sani; Rusnita Hamdan; Zulaiha Ali Othman; Azuraliza Abu Bakar (2023). Descriptive statistics for B40 clustering model. [Dataset]. http://doi.org/10.1371/journal.pone.0255312.t010
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mariah Abdul Rahman; Nor Samsiah Sani; Rusnita Hamdan; Zulaiha Ali Othman; Azuraliza Abu Bakar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Descriptive statistics for B40 clustering model.

  11. Research Data (Parental Stress and Depression Symptoms among B40 Wives of...

    • figshare.com
    pdf
    Updated Jul 27, 2022
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    Zarinah Arshat; Nurul Saidatus Shaja'ah Ahmad Shahril; Haikal Anuar Adnan (2022). Research Data (Parental Stress and Depression Symptoms among B40 Wives of Drug Addicts in Malaysia: Resilience as a Mediator) [Dataset]. http://doi.org/10.6084/m9.figshare.20381511.v1
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    pdfAvailable download formats
    Dataset updated
    Jul 27, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Zarinah Arshat; Nurul Saidatus Shaja'ah Ahmad Shahril; Haikal Anuar Adnan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    This data was used for an article published titled "Parental Stress and Depression Symptoms among B40 Wives of Drug Addicts in Malaysia: Resilience as a Mediator"

  12. Dimensions, indicators and measure attributes identified from the B40...

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Mariah Abdul Rahman; Nor Samsiah Sani; Rusnita Hamdan; Zulaiha Ali Othman; Azuraliza Abu Bakar (2023). Dimensions, indicators and measure attributes identified from the B40 clustering model. [Dataset]. http://doi.org/10.1371/journal.pone.0255312.t011
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mariah Abdul Rahman; Nor Samsiah Sani; Rusnita Hamdan; Zulaiha Ali Othman; Azuraliza Abu Bakar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dimensions, indicators and measure attributes identified from the B40 clustering model.

  13. Value of subsidy for hajj per pilgrim in Malaysia 2014-2024

    • statista.com
    Updated Jul 23, 2024
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    Statista (2024). Value of subsidy for hajj per pilgrim in Malaysia 2014-2024 [Dataset]. https://www.statista.com/statistics/964498/hajj-subsidy-per-pilgrim-malaysia/
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    Dataset updated
    Jul 23, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Malaysia
    Description

    In 2024, the amount of government subsidy for hajj pilgrimage for one pilgrim was 14,148 Malaysian ringgit. The amount applies to all pilgrims from non B40 group. The B40 group, or low-income group, received higher subsidy as of 2022. The hajj pilgrimage was postponed in 2020 and 2021 due to the COVID-19 pandemic.

  14. f

    Reasons for not registering as PeKa B40 provider (n = 179).

    • plos.figshare.com
    xls
    Updated Oct 17, 2023
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    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid (2023). Reasons for not registering as PeKa B40 provider (n = 179). [Dataset]. http://doi.org/10.1371/journal.pone.0292516.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 17, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Reasons for not registering as PeKa B40 provider (n = 179).

  15. f

    Factors associated with the acceptance and level of participation of...

    • plos.figshare.com
    xls
    Updated Oct 17, 2023
    + more versions
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    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid (2023). Factors associated with the acceptance and level of participation of Malaysian GPs towards future NHFS (Simple linear regression). [Dataset]. http://doi.org/10.1371/journal.pone.0292516.t008
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 17, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    Factors associated with the acceptance and level of participation of Malaysian GPs towards future NHFS (Simple linear regression).

  16. f

    Factors associated with the acceptance and level of participation Malaysian...

    • plos.figshare.com
    xls
    Updated Oct 17, 2023
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    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid (2023). Factors associated with the acceptance and level of participation Malaysian GPs in future NHFS (Independent t-test). [Dataset]. http://doi.org/10.1371/journal.pone.0292516.t009
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 17, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mohammad Husni Jamal; Aznida Firzah Abdul Aziz; Azimatun Noor Aizuddin; Syed Mohamed Aljunid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    Factors associated with the acceptance and level of participation Malaysian GPs in future NHFS (Independent t-test).

  17. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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(2020). Mean Monthly Household Gross Income of T20, M40 and B40 of households by strata, Malaysia - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/mean-monthly-household-gross-income-of-t20-m40-and-b40-of-households-by-strata-malaysia

Mean Monthly Household Gross Income of T20, M40 and B40 of households by strata, Malaysia - Dataset - MAMPU

Explore at:
Dataset updated
Jul 28, 2020
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Malaysia
Description

This dataset shows the number of Mean Monthly Household Gross Income of top 20%, middle 40% and bottom 40% of households by strata, 2002 - 2019, Malaysia. Source : DEPARTMENT OF STATISTICS MALAYSIA

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