In 2023, Malaysian employees between the ages of 40 and 44 years old earned the highest average monthly salary, at ***** Malaysian ringgit. With a retirement age of just 60 years old, Malaysian workers could look forward to earning more monthly average wage starting from around 40 years old. Economic outlook Malaysia is one of the biggest and strongest economies of South-East Asia, with a relatively low unemployment rate. The average monthly salary steadily increased from ***** Malaysian ringgit in 2014 to ***** Malaysian ringgit in 2023. Employees with a university degree could expect to earn almost twice as much. Can wages keep up with the cost of living? However, when seen in the context of rising living costs, wages in Malaysia have not been able to keep up. Despite having a relatively low inflation rate, prices have still gone up. Malaysians spent more than ** percent of their income on housing and food. In May 2022, the Malaysian government increased the minimum wage to 1,500 Malaysian ringgit monthly, or 7.21 Malaysian ringgit hourly, in the hopes of easing the financial burdens of its citizens.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perak data was reported at 43.316 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perak data is updated yearly, averaging 43.316 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perak data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Malaysia HIBAS: % of Monthly Gross Income: Top 20% data was reported at 46.200 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20% data is updated yearly, averaging 46.200 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20% data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

In 2022, the median wealth per adult in Malaysia was at around *** thousand U.S. dollars. In that year, ** percent of the adult population had wealth valued at under ten thousand U.S. dollars. Malaysia was ranked in the upper middle income group of countries.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Kuala Lumpur data was reported at 44.989 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Kuala Lumpur data is updated yearly, averaging 44.989 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Kuala Lumpur data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perlis data was reported at 40.713 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perlis data is updated yearly, averaging 40.713 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Perlis data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Abstract

The Household Income and Basic Amenities Survey (HIS/BA) 2012 is the latest income survey carried out by the Department of Statistics, Malaysia after the last survey in 2009. The survey is implemented twice in five years.

Data from this survey are used by the government as inputs for the formulation of national development plan especially in preparing for Tenth Malaysia Plan.

Objective of survey The main objectives of the survey are as follows: • collect information on income distribution pattern of households; • identify the poverty groups; and • identify the accessibility of basic amenities by poor households.

Geographic coverage

The survey covered both urban and rural areas in Malaysia except the Orang Asli Enumeration Blocks (EBs) in Peninsular Malaysia. Usually, the EBs that lie in the interior areas are not included in the sampling frames. However, for the latest survey, the Department expanded its coverage to include these EBs.

Analysis unit

• Households
• Individuals

Universe

The survey covered households staying in private living quarters (LQ) only. The institutional households, that is, those living in hostels, hotels, hospitals, old folk’s homes, military and police barracks, prisons, welfare homes and other institutions were excluded from the coverage of the survey.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Twice in 5 years

Sampling procedure

Sampling Frame The frame used for the selection of sample for HIS/BA 2012 was based on the Household Sampling Frame which was made up of Enumeration Blocks (EBs) created for the 2010 Population and Housing Census. EBs are geographical contiguous areas of land with identifiable boundaries. On average, each EB contains about 80 to 120 living quarters. Generally, all EBs are formed within gazetted boundaries i.e. within administrative districts, mukim or local authority areas.

The EBs in the sampling frame are also classified by urban and rural areas. Urban areas are as defined in the 2010 Population and Housing Census. Urban areas are gazetted areas with their adjoining built-up areas which had a combined population of 10,000 or more.

Sample Design A two-stage stratified sampling design was adopted. Primary strata (made up of states) and secondary strata (made up of urban and rural, and comprised district/ resident). Samples are drawn independently within each level of the secondary strata. The units for first stage sample selection are the EBs while the second stage units are the LQs within the EBs. All households and persons within the selected LQs are canvassed. At every stage of selection, the units are selected systematically with equal probability within each level of the secondary strata.

Sample Size The sample size required is based on the relative standard error of the previous survey for each strata and state. Other factors such as cost and availability of staff are also taken into consideration in determining the sample size.

Note: Detailed sampling design information is presented in the final report.

Mode of data collection

Face-to-face [f2f]

Sampling error estimates

Reliability of the data Data obtained from survey or research based on sample selection has a probability of encountering two types of errors. These are sampling and nonsampling errors.

(i) Sampling error Sampling error is a result of estimating data based on a probability sampling, not on census. Such error in statistics is termed as relative standard error (RSE) and is given in percentage. This is used as an indicator to the precision of the parameter under study. In other words, it reflects the extent of variation with other sample-based estimates.

For the HIS/BA 2012, the mean monthly household income for Malaysia was RM5,000 with an RSE of 0.6 per cent. In other words, the standard error (SE) is approximately RM29. Assuming that the mean household income is normally distributed, the confidence interval for the estimated mean income can be calculated. Based on a 95 per cent confidence level (alpha=0.05), the mean monthly household income was found to be in the range of RM4,943–RM5,057 per month.

(ii) Non-sampling error To ensure high quality data, several steps were taken to keep non-sampling errors to a minimum. Unlike sampling errors, these errors cannot be measured and can only be overcome through several administrative procedures. These errors may arise through incomplete survey coverage, weaknesses in the frame, feedback errors, non-response errors and also errors during processing such as editing, coding and data capture.

Response errors may occur due to difference and difficulty in interpreting questions, be it on the part of the enumerator or respondent. To minimize these errors, intensive training was conducted for the enumerators as well as the supervisors. In addition, random checks were carried out on households that were already canvassed by the enumerators to ensure the validity of the information recorded. To ensure the completeness of the survey coverage, the sampling frame is frequently updated and the selections of living quarters were done after the completion of EB listing exercise.

Non-response errors occur due to reasons such as vacant house, ‘no one at home’, refusal to cooperate or not qualified for the survey. To overcome such cases, sample size in the survey was estimated after taking into consideration all probabilities encountered as mentioned earlier.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

HIBAS: % of Monthly Gross Income: Top 20%: Selangor data was reported at 44.632 % in 2016. HIBAS: % of Monthly Gross Income: Top 20%: Selangor data is updated yearly, averaging 44.632 % from Dec 2016 (Median) to 2016, with 1 observations. HIBAS: % of Monthly Gross Income: Top 20%: Selangor data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pulau Pinang data was reported at 42.812 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pulau Pinang data is updated yearly, averaging 42.812 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pulau Pinang data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

In 2025, **** percent of the Malaysian population were classified as Bumiputera, **** percent were classified as ethnic Chinese, and *** percent as ethnic Indians. Those who do not fall under these three main ethnic groups are classified as ‘Other.’ Malaysia is a multi-ethnic and multi-religious society with three main ethnicities and language groups. Who are Malaysia’s Bumiputera? Bumiputera, meaning sons of the soil, is a term used to categorize the Malays, as well as the indigenous peoples of Peninsular Malaysia, also known as "orang asli", and the indigenous peoples of Sabah and Sarawak. As 2024, the Bumiputera share of the population in Sabah was ** percent, while that in Sarawak was **** percent. Thus, the incorporation of the states of Sabah and Sarawak during the formation of Malaysia ensured that the ethnic Malays were able to maintain a majority share of the Malaysian population. Bumiputera privileges and ethnic-based politics The rights and privileges of the Malays and the natives of Sabah and Sarawak are enshrined in Article 153 of Malaysia’s constitution. This translated, in practice, to a policy of affirmative action to improve the economic situation of this particular group, through the New Economic Policy introduced in 1971. 50 years on, it is questionable whether the policy has achieved its aim. Bumiputeras still lag behind the other ethnic two major groups in terms of monthly household income. However, re-thinking this policy will certainly be met by opposition from those who have benefitted from it.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pahang data was reported at 41.093 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pahang data is updated yearly, averaging 41.093 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Pahang data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

This dataset provides values for PERSONAL INCOME TAX RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2022, the East Malaysian state of Sabah had the highest rate of poverty in Malaysia, with **** percent of the population living below the poverty line. In 2019, Malaysia revised its national poverty line income, increasing it from 980 Malaysian ringgit to 2,208 Malaysian ringgit. Sabah is one of Malaysia's most rural and least developed states.

IntroductionChronic kidney disease (CKD) is a major public health issue with significant socioeconomic impacts. In Malaysia, the prevalence of CKD in 2018 was 15%. Complications of CKD such as anaemia, mineral bone disease, and infections led to frequent hospitalizations resulting in work disability and unemployment. To date, there is no data of employment status of CKD patients in Malaysia.MethodsA cross-sectional study of patients with advanced CKD (stage 4 and 5 non-dialysis) treated in our centre. We interviewed those aged 18 to 60 years old who were selected based on random sampling of their employment status and associated factors. Work disabilities and quality of life were assessed using work productivity and activity impairment (WPAI-GH) questionnaire and kidney disease and quality of life (KDQOL-36) questionnaire. These questionnaires were assisted by the main investigators to aid participants in facilitating their response process.ResultA total of 318 patients recruited, 53.5% were males, with a mean age of 49.0 ± 9.0 years old. The main cause of CKD was diabetes (67.0%) followed by hypertension (11.3%). Majority of them were obese (55.3%) with a mean body mass index of 28.81 ± 6.3 kg/m2. The mean household income was RM 4669.50 ± 3034.75 (USD1006.27 ± 653.99). The employment rate was 50% (n = 159). 86% of the unemployed patients were in B40 income category. Multiple Logistic Regression was performed on the significant factors affecting employment status showed one year increase in age increased 6.5% odds to be unemployed. Female and dyslipidaemia had 2.24- and 2.58-times higher odds respectively to be unemployed. Meanwhile, patients with tertiary level of education were 81% less odds to be unemployed. Patients with advanced CKD had a mean percentage of 24.35 ± 15.23 work impairment and 13.36 ± 32.34 mean percentages of face absenteeism due to the disease burden. Furthermore, patients who were unemployed had significant perceived symptoms and problem lists, effects, and burden of kidney disease (p

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Sarawak data was reported at 45.031 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Sarawak data is updated yearly, averaging 45.031 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Sarawak data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Terengganu data was reported at 40.799 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Terengganu data is updated yearly, averaging 40.799 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Terengganu data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Putrajaya data was reported at 46.136 % in 2016. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Putrajaya data is updated yearly, averaging 46.136 % from Dec 2016 (Median) to 2016, with 1 observations. Malaysia HIBAS: % of Monthly Gross Income: Top 20%: Putrajaya data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H037: Household Income and Basic Amenities Survey: Percentage of Monthly Gross Income: Household Group: by State.

In 2023, the urban population in Malaysia reached approximately 27.65 million people. In the last decade, the urban population in Malaysia has increased by more than five million people, indicating an upward trend in urbanization. Rapid urbanization In 2023, approximately 78 percent of Malaysia’s population lived in urban areas and cities. This puts Malaysia as one of Southeast Asia’s most urbanized countries. The metropolitan area of Greater Kuala Lumpur had a population of more than eight million in that year, making it Malaysia’s biggest urban area. However, about twice as many Malaysians lived in cities with half a million inhabitants or less. Urban-rural divide For many Malaysians, urbanization brought about more and better economic opportunities and job prospects. The average monthly income for urban households was more than 3,000 Malaysian ringgit higher than that of rural households. Poverty rates in rural areas were also significantly higher than that in urban areas. Even so, the countryside still holds a nostalgic place in the hearts of Malaysians, encapsulated in the romanticized idea of the ‘kampung’ or Malay village.