In 2022, around **** percent of Malaysians had monthly household income between two thousand to five thousand Malaysian ringgit. By comparison, around **** percent of people in Malaysian had more than ****** Malaysian ringgit of monthly household income. The current

In 2022, the highest average monthly household income was in the Wilayah Persekutuan Putrajaya, with around ****** Malaysian ringgit. Wilayah Persekutuan Kuala Lumpur, the capital city and also a federal territory, came second with around ****** Malaysian ringgit in monthly average household income.

Abstract

The Household Income/Basic Amenities Survey (HIS/BA) 2009 is the latest income survey carried out by the Department of Statistics, Malaysia after the last survey conducted in 2007. The survey is implemented twice in five years. The publication provides data on income, poverty and basic amenities at national and state levels for citizens only.

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

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

Geographic coverage

The survey covered both urban and rural areas in Malaysia except the Orang Asli Enumeration Block (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). The institutional households, that is, those living in hostels, hotels, hospitals, old folks 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]

Sampling procedure

Sampling frame

The frame used for the selection of sample for HIS/BA 2009 was based on the National Household Sampling Frame (NHSF) which was made up of EBs created for the 2000 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 2000 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 at the time of the 2000 Population and Housing Census. All other gazetted areas with a population of less than 10,000 persons and non-gazetted areas are classified as rural.

Built-up areas are defined as areas contiguous to a gazetted area and have at least 60 per cent of their population (aged 10 years and over) engaged in nonagricultural activities as well as having modern toilet facilities in their housing units.

Urbanisation is a dynamic process and keeps changing in line with progress and development. Thus the urban areas for the 1991 and 2000 censuses do not necessarily refer to the same areas, as areas fulfilling the criteria of urban continue to increase or grow over time.

Sample design

A two-stage stratified sampling design was adopted and the levels of stratification are as follows: i) Primary stratum - made up of states in Malaysia ii) Secondary stratum - made up of urban and rural as defined in para 6.5 and formed within primary stratum

Samples are drawn independently within each level of the secondary stratum. 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 stratum.

Sample size

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

The sampling procedures are more fully described in "Malaysia Household Income and Basic Amenities Survey 2009 - Report" pp. 93-96.

Mode of data collection

Face-to-face [f2f]

Sampling error estimates

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 2009, the mean monthly gross household income for Malaysia was RM4,025 with an RSE of 0.63 per cent. In other words, the standard error (SE) is approximately RM25. Assuming that the mean household income is normally distributed, the confidence interval for the estimated mean income can be calculated. Based on a 95 percent confidence level (alpha = 0.05), the mean monthly household income was found to be in the range of RM3,975.75–RM4,074.89 monthly.

A follow-up of the 1976-1977 MFLS-1 dataset covering the respondents'' and spouses'' marriage, fertility, employment, education and migration histories as well as extensive information on the household economy. The MFLS-2 contains a supplementary sample of persons age 50 or older. The data permit analysis of intergenerational transfers to the elderly and their covariates; the living arrangements of the elderly; the health of the elderly; labor supply, occupation and retirement status of the elderly; and their migration patterns. This supplement fills the gap left by many standard sources of demographic and economic information about Third World populations, such as fertility surveys and labor force surveys, which effectively exclude the elderly. Field work for MFLS-2 began in Aug. 1988 and was completed in Jan. 1989. The survey was fielded in four samples: * The Panel Sample Women who were the primary respondents to the MFLS-1, who at that time (1976) were ever-married women aged 50 or younger. There are 926 panel households in MFLS-2, a follow-up rate of 72%. * The Children Sample Children aged 18 or older in 1988 of the women interviewed as primary respondents for MFLS-1; i.e. adult children of the women eligible for the MFLS-2 Panel sample. There were interviews with one child, selected at random, inside the Panel household and two children, selected at random, living elsewhere in Peninsular Malaysia. There are 1,136 respondents in the Children sample. * The New Sample A sample of households with a woman aged 18-49 (regardless of her marital status) or an ever-married woman under age 18. There are 2,184 respondents in MFLS-2 New Sample. * The Senior Sample Selected households with a person age 50 or over. There are 1,357 respondents in the Senior Sample. Data Availability: The MFLS-2 (and MFLS-1) data files and documentation are available on-line or from NACDA at ICPSR as Study No. 9805. * Dates of Study: 1988-1989 * Study Features: International * Sample Size: Seniors (aged 50+): 1,357 Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09805

In 2022, ethnic Chinese households had the highest mean monthly household income in Malaysia, at around ****** Malaysian ringgit. This was more than ***** 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 ***** 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.

Malaysia Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 16.500 % in 2021. This records a decrease from the previous number of 17.000 % for 2018. Malaysia Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 19.250 % from Dec 1984 (Median) to 2021, with 14 observations. The data reached an all-time high of 21.100 % in 1997 and a record low of 15.900 % in 2013. Malaysia Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Malaysia Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 8.300 % in 2015. Malaysia Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 8.300 % from Dec 2015 (Median) to 2015, with 1 observations. Malaysia Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Abstract

THE CBI PDM Household Survey was conducted in Malaysia between December, to January, 2021. In Malaysia, refugees live in a very challenging environment with limited rights to health, education and work. As the Malaysian government does not provide refugees with any monetary support, refugees depend on low-income work to provide for their families and for themselves. As there are approximately 150,000 refugees in Malaysia, the CBI program is targeted to the most vulnerable groups, with a household income below the national poverty line, women and girls at risk, children and adolescents at risk and persons with serious medical conditions. Assistance to refugees who have been detained and have not managed to earn sufficient funds during their sentence is also provided.

UNHCR uses Post-Distribution Monitoring (PDM) as a mechanism to collect refugees' feedback on the quality, sufficiency, utilization and effectiveness of the assistance items they receive. The underlying principle behind the process is linked to accountability, as well as a commitment to improve the quality and relevance of support provided, and related services. UNHCR increasingly uses Cash-Based Interventions (CBIs) as a preferred modality for delivering assistance, offering greater dignity and choice to forcibly displaced and stateless persons in line with UNHCR's core protection mandate. In order to ensure that the cash assistance provided meets the intended programme objectives and that desired outcomes are achieved, UNHCR conducts regular post-distribution and outcome monitoring with a sample or all of refugee recipients.

Geographic coverage

The survey is conducted in Kuala Lumpur, Pahang, Penang and Selangor.

Analysis unit

Households

Universe

The total population spans all beneficiaries subject to the last two Cash-Based Intervention in 2020 in Malaysia.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey's objective was to deliver data of all refugee households that were beneficiaries of the last two rounds of cash-based interventions implemented in 2020. The total number of households that received cash-based interventions in that period was 149 and all recipients were asked to answer the survey.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

Out of the 149 households, 131 answered and gave their consent to participate in the survey.

The relationships between cigarette affordability, consumer income levels and distribution, and tax increases are complex and underexplored. This study investigates different ways of calculating the Relative Income Price (RIP) measure of affordability using Malaysia as a case study. We calculate cigarette affordability in Malaysia between 2009–2019 using government data, and multiple RIP variants. The conventional RIP calculation relies on 2,000 sticks and GDP (henceforth standard RIP). We explore that and other variants that use annual cigarette consumption estimates and/or proportions of various financial measures of wealth in both rural and urban areas. Our findings indicate broadly consistent trends in cigarette affordability across all methods. From 2009 to 2012, there was a slight decrease in the percentage of wealth required to purchase cigarettes, followed by an increase in 2015 and 2016, and then another decline, suggesting a recent trend toward increased affordability. Using the standard RIP method, 0.9 percentage points(pp) more of per capita GDP was required between 2009 and 2016, but, by 2019 it was 0.1pp less than in 2016. However, Household Income Per Capita (HIPC) and Household Expenditure Per Capita (HEPC) provide a more nuanced perspective on cigarette affordability compared to GDP per capita, as they reveal larger shifts in affordability. The conventional 2,000 sticks method using HIPC from 2009 to 2016 indicated 0.3pp more of income was required to purchase cigarettes, but by 2019, it was 1.0pp less than in 2016. Using HIPC with actual consumption estimates, smokers required approximately 0.9pp more of average income to purchase cigarettes between 2014 and 2016, but 2.5pp less from 2016 to 2019. Actual consumption estimates offer insight into smokers’ ability to offset higher purchase costs by adjusting consumption patterns without quitting. We conclude that to address issues related to cigarette affordability, the Malaysian government should consider increasing tobacco tax vis-à-vis income growth.

IntroductionAs the rate of end-stage kidney disease rises, there is an urgent need to consider the catastrophic health expenditure of post-transplantation care. Even a small amount of out-of-pocket payment for healthcare can negatively affect households’ financial security. This study aims to determine the association between socioeconomic status and the prevalence of catastrophic health expenditure in post-transplantation care.MethodA multi-centre cross-sectional survey was conducted in person among 409 kidney transplant recipients in six public hospitals in the Klang Valley, Malaysia. Catastrophic health expenditure is considered at 10% out-of-pocket payment from household income used for healthcare expenditure. The association of socioeconomic status with catastrophic health expenditure is determined via multiple logistic regression analysis.Results93 kidney transplant recipients (23.6%) incurred catastrophic health expenditures. Kidney transplant recipients in the Middle 40% (RM 4360 to RM 9619 or USD 1085.39 –USD 2394.57) and Bottom 40% (RM 9619 or > USD 2394.57) income group. Kidney transplant recipients in the Bottom 40% and Middle 40% income groups were more susceptible to catastrophic health expenditure at 2.8 times and 3.1 times compared to higher-income groups, even under the care of the Ministry of Health.ConclusionUniversal health coverage in Malaysia cannot address the burden of out-of-pocket healthcare expenditure on low-income Kidney transplant recipients for long-term post-transplantation care. Policymakers must reexamine the healthcare system to protect vulnerable households from catastrophic health expenditures.

As of July 2024, **** 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 of July 2023, 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.

Total mean and median household costs for a single malaria episode in 2023 United States dollars (n=152). Results in Malaysian Ringgits are presented in Table B in S1 Text.

Abstract

Household Expenditure Survey 2009/10 was used to compile information on the level and patterns of consumption expenditure of private households. It was also used to update the weighting patterns and to determine the items in the basket of goods and services for the compilation of the Consumer Price Index for Malaysia.

The objectives of HES are to: - gather information on the level and pattern of consumption expenditure by household on a comprehensive range of goods and services; - determine the goods and services to be included in the basket of the Consumer Price Index (CPI); - update the CPI weights which is a measure of inflation rate in the country.

Geographic coverage

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

Analysis unit

• Households
• Individuals

Universe

The survey only covered households staying in private living quarters (LQ). The institutional households, that is, those living in hostels, hotels, hospitals, old folks 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

Once in 5 years

Sampling procedure

Sampling Frame The frame for HES 2009/10 sample selection was based on the National Household Sampling Frame (NHSF) which was made up of EBs created for the 2000 Population and Housing Census. EBs are geographically contiguous areas of land with identifiable boundaries. On average, each EB contains about 80-120 LQs. Generally, all EBs are formed within gazetted boundaries, i.e. within administrative district, mukim or local authority areas.

The EBs in the sampling frame were also classified by urban and rural areas. Urban areas were as defined in the 2000 Population and Housing Census. Urban areas are gazetted areas with their adjoining built-up areas which has a combined population of 10,000 or more at the time of 2000 Population and Housing Census. All other gazetted areas with a population of less than 10,000 persons and non-gazetted areas are classified as rural. Built-up areas are defined as areas contiguous to a gazetted area and has at least 60 per cent of their population (aged 10 years and over) engaged in non-agricultural activities as well as having modern toilet facilities in their housing units.

Urbanisation is a dynamic process and keeps changing in line with progress and development. Thus, urban areas for the 1991 and 2000 censuses may not necessarily refer to the same areas, as these urban areas continue to increase and grow over time.

The classification of areas by stratum is as follows:

Stratum     Number of Population
(a) Metropolitan    75,000 and over
(b) Urban Large   10,000 to 74,999
(c) Urban Small   1,000 to 9,999
(d) Rural    All other areas

For sampling purposes, the above broad classification was found to be adequate for all states and W.P. Kuala Lumpur, Putrajaya and Labuan. However, for Sabah and Sarawak, due to inaccessibility, the rural stratum had to be further stratified based on the time taken to reach the area from the nearest urban centre.

For purposes of tabulation by urban and rural, the strata were combined as follows: Metropolitan + urban large = Urban Urban small + all rural = Rural

Sampling Design A two-stage stratified sampling design was adopted and the level of stratification is as follows: Primary stratum - made up of the states in Malaysia, including W.P. Kuala Lumpur, Putrajaya and Labuan. Secondary stratum - made up of selected towns, others towns and rural stratum formed within the primary stratum.

Mode of data collection

Face-to-face [f2f]

Sampling error estimates

Data obtained from surveys or research based on probability sample may encounter two types of errors, i.e. sampling and non-sampling errors.

Sampling Error Sampling error is a result of estimating data based on probability sampling, not on census. Statistically, these errors are referred to as Relative Standard Errors, denoted by RSE and expressed in percentage. This error is an indication of the precision of the parameter under study. In other words, it reflects the extent of variation as compared with other sample-based estimates.

For HES 2009/10, the average monthly household expenditure for Malaysia is RM2,190 with RSE 1.2 per cent. In absolute terms, the standard error (SE) is approximately RM26. With the assumption that the average monthly household expenditure is normally distributed, the confidence interval for the estimated average monthly household expenditure can be calculated. Based on a 95 percent confidence level (alpha=0.05), the average monthly household expenditure was found to be in the range of RM2,139-RM2,241 per month.

Non-sampling Error To ensure high quality data, several steps were taken to minimise non-sampling errors. Unlike sampling errors, these errors cannot be measured and can only be overcome through several administrative procedures. These errors may arise as a result of incomplete survey coverage, weakness in the frame, feedback errors, non-response errors, errors during data processing such as editing, coding and data capture.

Response errors are likely to occur due to differences and difficulties in interpreting the questions, be it on the part of the enumerators or respondents. To minimise these errors, intensive trainings were conducted for the enumerators as well as for the supervisors. In addition, random checks were carried out on households that were already canvassed by the enumerators to verify the validity of the information recorded. To ensure the completeness of the survey coverage, the sampling frame is frequently updated and the LQs were selected after the completion of EB listing exercise.

For HES 2009/10, there were no substitution for non-response cases, such as refusal to co-operate or no one at home. The calculation of weights was based on actual responses. In order to obtain respondents' co-operation, a wide scale publicity of the survey was launched through the electronic and print media. To reduce the editing and processing errors, several consistency checks were created, either manually or computer aided, to ensure the quality and reliability of data.

Marginal costs, standard errors, and 95% confidence intervals of factors associated with variability with total household costs from the generalized linear model that included all patients (N=151). All costs are in 2023 United States dollars.

Mean (standard deviation) number of days away from usual activity per patient and their household due to an episode of malaria (N=152). All severe cases in the study were due to Plasmodium knowlesi.

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.

Abstract

The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

Geographic coverage

13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

Analysis unit

-Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

Research Protocols:

• National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

Mode of data collection

Face-to-face [f2f]

Research instrument

A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

Cleaning operations

Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.