Malawi NSO Projection: Population: Female data was reported at 22,558,434.000 Person in 2050. This records an increase from the previous number of 22,028,761.000 Person for 2049. Malawi NSO Projection: Population: Female data is updated yearly, averaging 14,551,772.000 Person from Dec 2017 (Median) to 2050, with 34 observations. The data reached an all-time high of 22,558,434.000 Person in 2050 and a record low of 9,101,565.000 Person in 2018. Malawi NSO Projection: Population: Female data remains active status in CEIC and is reported by National Statistics Office of Malawi. The data is categorized under Global Database’s Malawi – Table MW.G001: Population: Projection: National Statistical Office of Malawi.

Malawi NSO Projection: Population data was reported at 45,180,255.000 Person in 2050. This records an increase from the previous number of 44,110,905.000 Person for 2049. Malawi NSO Projection: Population data is updated yearly, averaging 28,989,762.500 Person from Dec 2017 (Median) to 2050, with 34 observations. The data reached an all-time high of 45,180,255.000 Person in 2050 and a record low of 17,931,637.000 Person in 2018. Malawi NSO Projection: Population data remains active status in CEIC and is reported by National Statistics Office of Malawi. The data is categorized under Global Database’s Malawi – Table MW.G001: Population: Projection: National Statistical Office of Malawi.

Central Bureau of Statistics (CBS) was established in 1959 under Statistics Act, 2015 BS as the central agency for the collection, consolidation, processing, analysis, publication and dissemination of statistics. It is under the National Planning Commission Secretariat (NPCS) of Nepal and serves as a national statistical organization of Government of Nepal. It generates timely and reliable socio-economic statistics mainly through the operation of censuses and surveys. The main objective of setting up CBS is to avail data to NPCS and other Government agencies for the formulation of national plans, policies and also produces national account estimates to measure the economic growth of the country. It carries out different household surveys and censuses regularly to assess the socio- economic condition of the country. The major tasks, which CBS has been performing, are decennial population census, agriculture census and quinquennial manufacturing establishment census. In addition, many household surveys like living standard surveys, labor force surveys and multiple indicator surveillance are indispensable tasks, which CBS has been accomplishing. The role of the CBS is always decisive for the effective functioning of the national statistical system as a whole. It promotes collaborative research efforts among members of academic community, data producers and users. It has a prominent role in developing statistical system and maintaining statistical standard in the country. Statistics Act, 2079 came into effect on August, 2022. As the provision of the act, CBS has upgraded to the National Statistics Office (NSO). The National Statistics Office (NSO) is the central statistical organization of the country responsible for the production and dissemination of official statistics. Data Portal is a data visualization and analysis tool for Nepal, built to be used by all sectors of society. Create infographics, analyze and visualize the data you’re working with, and link directly to the source data.

The UK's largest independent producer of official statistics and the recognised national statistical institute of the UK.

Thailand Domestic Sales: > 200 Persons: Total data was reported at 223,795,909.284 THB th in Jun 2018. This records a decrease from the previous number of 235,266,724.369 THB th for Mar 2018. Thailand Domestic Sales: > 200 Persons: Total data is updated quarterly, averaging 142,943,152.225 THB th from Mar 2012 (Median) to Jun 2018, with 26 observations. The data reached an all-time high of 235,266,724.369 THB th in Mar 2018 and a record low of 115,667,212.910 THB th in Sep 2012. Thailand Domestic Sales: > 200 Persons: Total data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H007: Domestic Sales: National Statistical Office.

This study investigates the impact on Thai students’ health of commuting to school, both in terms of time and distance. The individual-level dataset used in this study is obtained from the National Statistical Office (NSO) and limited to students aged from 9 to 18 years old, with 25,461 respondents. While the data were collected in 2016, with mostly unchanged commuting behaviors of Thai students, our results can reflect current health impacts from school commutes. The data indicate that traffic in Bangkok causes students to commute longer to schools than in other provinces. The results from the ordered logistic regression consistently show that commuting time has stronger negative impacts on health than commuting distance does. In other provinces, our results show that long commuting time and distance negatively affect physical and mental health of students. The present study also indicates that investigating either commuting distance or commuting time could bias the results in some sequences.

Estimates for time spent by employed parents carrying out their daily tasks

This study investigates the impact on Thai students’ health of commuting to school, both in terms of time and distance. The individual-level dataset used in this study is obtained from the National Statistical Office (NSO) and limited to students aged from 9 to 18 years old, with 25,461 respondents. While the data were collected in 2016, with mostly unchanged commuting behaviors of Thai students, our results can reflect current health impacts from school commutes. The data indicate that traffic in Bangkok causes students to commute longer to schools than in other provinces. The results from the ordered logistic regression consistently show that commuting time has stronger negative impacts on health than commuting distance does. In other provinces, our results show that long commuting time and distance negatively affect physical and mental health of students. The present study also indicates that investigating either commuting distance or commuting time could bias the results in some sequences.

Abstract

The objective of the WMS is to provide rapid information about the selected core indicators in the population, as well as monitoring changes over time when repeated on a regular basis. More specifically, the objectives of the WMS are: · Elaborating main indicators for monitoring MDG's, MGDS indicators and other indicators on social welfare and basic needs of the population and various subgroups · Monitoring changes over time in the MDG's, MGDS indicators and the other indicators used to monitor the development of living conditions and poverty in the population and various target groups · Providing a database for social research. · Elaborating on numerous sector programs aimed at improving the welfare of the population across the country. In order to prepare these programs, it is necessary to identify the problems to be addressed by the policies and to know to which extent the population is affected by these problems

The following are the contens covered in this survey: · Characteristics of the Household Members · Health · Education · Employment · Food Security · Housing Condition and Amenities · Poverty Predictors · Child health - Birth and anthropometric measures · Child health - Malaria protection and Treatment · Child health - Vaccination · HIV/AIDS Testing and Knowledge

Geographic coverage

National

Analysis unit

A living standards survey questionnaire with the following units of analysis: individuals, households, and children under 5 years of age.

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size - Selection process (e.g., probability proportional to size or over sampling) - Stratification (implicit and explicit) - Stages of sample selection (WMS 2006 contained 4,500 households and 350 Enumeration Areas [EAs] across the country drawn as a two stage design) - Design omissions in the sample - Level of representation (More EAs were included in the sample so as to provide estimates at regional level) - Strategy for absent respondents/not found/refusals (replacement or not) -(Sampling of the households was with replacement) - Sample frame used, and listing exercise conducted to update it -(WMS 2006 sample was drawn from the The Second Integrated Household Survey [IHS2] 20003/04 sample. Since the EAs were from IHS2, there was no listing of the households)

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic WMS were structured questionnaires based on the WM Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household characteristics, o health, education, employment, food security, poverty predictors, housing conditions and amenities, child health and anthropometric measures and HIV and AIDS knowledge.

In addition to the household questions, the questionnaire asked questions on children under age five. For children, the questionnaire was administered to the mother or caretaker of the child. The children's questions included children's characteristics, birth registration and early learning, vitamin A, malaria, immunization, and anthropometry.

The questionnaires were developed in English from the WMS Model Questionnaire.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource. Data processing for this WMS involved: - • Scanning and editing of questionnaires, using Eyes and Hands software • Consistency checks and data cleaning in SPSS • Designing tabulation programs in SPSS • Final table editing in Microsoft Excel.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2006 WMS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Thailand Domestic Sales: 26-30 Persons: Total data was reported at 20,557,054.527 THB th in Jun 2018. This records an increase from the previous number of 18,740,909.235 THB th for Mar 2018. Thailand Domestic Sales: 26-30 Persons: Total data is updated quarterly, averaging 14,555,159.705 THB th from Mar 2012 (Median) to Jun 2018, with 26 observations. The data reached an all-time high of 20,557,054.527 THB th in Jun 2018 and a record low of 11,507,910.480 THB th in Sep 2012. Thailand Domestic Sales: 26-30 Persons: Total data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H007: Domestic Sales: National Statistical Office.

Thailand Domestic Sales: YoY: 1-15 Persons: Total data was reported at 24.600 % in Jun 2018. This records an increase from the previous number of 23.600 % for Mar 2018. Thailand Domestic Sales: YoY: 1-15 Persons: Total data is updated quarterly, averaging 21.800 % from Mar 2013 (Median) to Jun 2018, with 22 observations. The data reached an all-time high of 27.000 % in Jun 2016 and a record low of 0.100 % in Sep 2014. Thailand Domestic Sales: YoY: 1-15 Persons: Total data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H007: Domestic Sales: National Statistical Office.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs) as well as the goals listed as part of the Malawi Growth and Development Strategy (MGDS).

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS3 sampling frame is based on the listing information and cartography from the 2008 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. It was decided to exclude the island district of Likoma from the IHS3 sampling frame, since it only represents about 0.1% of the population of Malawi, and the corresponding cost of enumeration would be relatively high. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS3 strata are composed of 31 districts in Malawi.

A stratified two-stage sample design was used for the IHS3.

Note: Detailed sample design information is presented in the "Third Integrated Household Survey 2010-2011, Basic Information Document" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collectd using four questionnaires: 1) Household Questionnaire 2) Agriculture Questionnaire 3) Fishery Questionnaire 4) Community Questionnaire

Cleaning operations

Data Entry Clerks Each IHS3 field team was assigned 1 data entry clerk to process completed questionnaires at the teams field based residence. Each data entry clerk was issued a laptop with the CSPro based data entry application, a printer to produce error reports on entered questionnaire, and flash disks for transferring files. The field based data entry clerk's primary responsibilities included: (1) receiving the completed questionnaires following the field supervisor's initial screening, (2) organizing and entering completed questionnaire in a timely manner, (3) generating and printing error reports for supervisor review, (4) modifying data after errors were resolved and authorized by the field supervisor, and (5) managing data files and local data back-ups. The data entry clerk was responsible for beginning initial data entry upon receipt of questionnaires from the field and generating error reports as quickly as possible after interviews were complete in the EA. When long distance travel to an enumeration area by the field team was required and the field team was required to spend multiple days away from their field residence the data entry clerk was required to travel with the team in order to maintain data processing schedules.

Field Based Data Entry and CAFE To better facilitate higher quality data and increase timely availability of data during the data capture process IHS3 utilized computer assisted field entry (CAFE). First data entry was conducted by field based data entry clerks immediately following completion of the team's daily field activities. Each team was equipped with 1 laptop computer for field based data entry using a CSPro-based application. The range and consistency checks built into the CSPro application was informed by the LSMS-ISA experience in Tanzania and Uganda, and the review of the IHS2 data. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Completed data was frequently relayed to the NSO central office in Zomba via email and tracked and processed upon receipt.

Double Data Entry Double data entry was implemented by a team of data entry clerks based at the NSO central office. Electronic data and questionnaires received from the field were cataloged by the Data Manager and electronic data loaded onto a central server to enable data entry verification on networked computers. To increase quality, the Data Entry Manager monitored the data verification staff and conducted quality assessments by randomly selecting processed questionnaires and comparing physical questionnaires to the result of double data entry. Data verification clerks were coached on inconsistencies when required.

Data Cleaning The data cleaning process was done in several stages over the course of field work and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field based field teams utilizing error reports produced by the data entry applications. Field supervisors collected reports for each enumeration area and household and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call backs while the team was still operating in the enumeration area when required. Corrections to the data were entered by the field based data entry clerk before transmitting data to the NSO central office.

Upon receipt of the data from the field, module and cross module checks were performed using Stata to identify systematic issues and, where applicable, field teams were asked to investigate, revise and resend data for questionnaires still in their possession. Revised data files were cataloged and then replaced previous version of the data.

After data verification by the headquarters' double data entry team, data from the first data entry and second data entry were compared. Cases that revealed large inconsistencies between the first and second data entry, specifically large amounts of missing case level data in the second data entry relative to the first data entry were completely reentered. Further, variable specific inconsistency reports were generated and investigated and corrected by the double data entry team. Additional cleaning was performed after the double data entry team cleaning activities where appropriate to resolve systematic errors and organize data modules for consistency and efficient use. Case by case cleaning was also performed during the preliminary analysis specifically pertaining to out of range and outlier variables.

All cleaning activities were conducted in collaboration with the WB staff providing technical assistance to the NSO in the design and implementation of the IHS3.

Commuting effects on mental health by school-home location in other provinces.

Abstract

The 2010 Population and Housing Census was Conducted between 11-17 November 2010. Over 750,000 household forms were completed by over 12,000 enumerators. More than 30,000 persons were directly involved in census conducting. The Population and Housing Census is the biggest event organized by the National Statistical Office. The unique feature of the Census is that it covers a wide range of entities starting from the primary unit of the local government up to the highest levels of the government as well as all citizens and conducted with the highest levels of organization. For the 2010 Population and Housing Census, the management team to coordinate the preparatory work was established, a detailed work plan was prepared and the plan was successfully implemented. The preliminary condition for the successful conduct of the Census was the development of a detailed plan. The well thought-out, step by step plan and carefully evidenced estimation of the expenditure and expected results were crucial for the successful Census. Every stage of the Census including preparation, training, enumeration, data processing, analysis, evaluation and dissemination of the results to users should be reflected in the Census Plan.

Geographic coverage

National

Analysis unit

• Household;
• Indivudual.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Processing System

The introduction of internet technology and GIS in the 2010 Population and Housing Census has made the census more technically advanced than the previous ones. Compared to the data processing of the 2000 Population and Housing Census the techniques and technological abilities of the NSO have advanced. The central office - National Statistical Office has used an internal network with 1000 Mbps speed, an independent internet line with 2048 Kbps speed and server computers with special equipments to ensure the reliable function of internal and external networks and confidentiality. The Law on Statistics, the Law on Population and Housing Census, the guidelines of the safety of statistical information systems and policies, the provisional guidelines on the use of census and survey raw data by the users, the guidelines on receiving, entering and validating census data have created a legal basis for census data processing.

The data-entry network was set up separately from the network of the organization in order to ensure the safety and confidentiality of the data. The network was organized by using the windows platform and managed by a separate domain controller. Computers where the census data will be entered were linked to this server computer and a safety devise was set up to protect data loss and fixing. Data backup was done twice daily at 15:10 hour and 22:10 hour by auto archive and the full day archive was stored in tape at 23:00 hour everyday.

The essential resources of important equipments and tools were prepared in order to provide continuous function of all equipment, to be able to carry out urgent repairs when needed, and to return the equipment to normal function. The computer where the census data would be entered and other necessary equipment were purchased by the state budget. For the data processing, the latest packages of software programs (CSPro, SPSS) were used. Also, software programs for the computer assisted coding and checking were developed on NET within the network framework.

INTERNET CENSUS DATA PROCESSING

One of the specific features of the 2010 Population and Housing Census was e-enumeration of Mongolian citizens living abroad for longer period. The development of a web based software and a website, and other specific measures were taken in line with the coordination of the General Authority for State Registration, the National Data Centre, and the Central Intelligence Agency in relation to ensuring the confidentiality of data. Some difficulties were encountered in sharing information between government agencies and ensuring the safety and confidentiality of census data due to limited professional and organizational experience, also because it was the first attempt to enumerate its citizens online.

The main software to be used for online registration, getting permission to get login and filling in the census questionnaire online as well as receiving a reply was developed by the NSO using a symphony framework and the web service was provided by the National Data Centre. Due to the different technological conditions for citizens living and working abroad and the lack of certain levels of technological knowledge for some people the diplomatic representative offices from Mongolia in different countries printed out the online-census questionnaire and asked citizens to fill in and deliver them to the NSO in Mongolia. During the data processing stage these filled in questionnaires were key-entered into the system and checked against the main census database to avoid duplication.

CODING OF DATA, DATA-ENTRY AND VALIDATION

Additional 136 workers were contracted temporarily to complete the census data processing and disseminate the results to the users within a short period of time. Due to limited work spaces all of them were divided into six groups and worked in two shifts with equipments set up in three rooms and connected to the network. A total of six team leaders and 130 operators worked on data processing. The census questionnaires were checked by the ad hoc bureau staff at the respective levels and submitted to the NSO according to the intended schedule.

These organizational measures were taken to ensure continuity of the census data processing that included stages of receiving the census documents, coding the questionnaire, key-entering into the system and validating the data. Coding was started on December 13, 2010 and the data-entry on January 7, 2011. Data entering of the post-enumeration survey and verification were completed by April 16, 2011. Data checking and validation started on April 18, 2011 and was completed on May 5, 2011. The automatic editing and imputation based on scripts written by the PHCB staff was completed on May 10, 2011 and the results tabulation was started.

This project was initiated in 2021 to generate modelled population estimates for Papua New Guinea (PNG) to support their census preparations. It was powered by the Australian Government through the PNGAus partnership, the United Nations Population Fund (UNFPA ) and the PNG National Statistical Office.

The project team combined recent 2019-2021 malaria bednet campaign data, urban structural listing 2021 data, and geospatial covariates to model and estimate population numbers at census unit level, and aggregate at other relevant administrative units (e.g., national, province, and districts) using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data hierarchy. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the years 2020-21. This time period corresponds to the malaria survey and urban structural listing survey observations (2019-2021; median year: 2020) and the period of the satellite imagery used to generate settlement footprints (2021). Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.

These data were produced by the WorldPop Research Group at the University of Southampton in collaboration with the National Statistical Office of PNG and UNFPA under the project called “Population-modelled estimation for Papua New Guinea in collaboration with the National Statistical Office, 2021-22” (PNG40-0000004504). The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Amy Bonnie with additional support from Tom Abbott, Tom McKeen, Heather Chamberlain, Ortis Yankey, Duygu Cihan and Assane Gadiaga. Project oversight was done by Attila Lazar and Andy Tatem. Household survey listing data were provided by the National Statistical Office, and the settlement footprint was generated by Planet.

Please, note that the same modelled population data (with minor rounding difference of 41 in the national total) can also be downloaded from the NSO’s website: https://www.nso.gov.pg/statistics/population/

How to cite this work WorldPop and National Statistical Office of Papua New Guinea. 2022. Census-independent population estimates for Papua New Guinea (2020-21), version 1.0. WorldPop, University of Southampton. DOI: 10.5258/SOTON/WP00763.

Abstract

The National Bureau of Statistics (NBS) was established with the Statistics Act of 2007 and the merger of the Federal Office of Statistics (FOS) and the National Data Bank (NDB). Nigeria has a Federal System of government with 36 States and a Federal Capital Territory and 774 Local Government Areas. Each Federal Ministry, Department and Agency has a Director of Statistics. Each state has a Director of Statistics and a Head of statistics Unit at the Local Government level. These and the Statistical Institutes constitute Nigeria's National Statistical System (NSS) which is coordinated by the NBS. The Nigeria National Data Archive was established to: Promote best practice and international standards for the documentation of microdata amongst data producers in the country Provide equitable access to microdata in the interest of all citizens Ensure the long term preservation of microdata and the related metadata, and their continued viability and usability The Data Archive holds NBS datasets from 1999 to the current year.

Analysis unit

Households, individuals, and establishments

Kind of data

Administrative records and survey data

This dataset provides national theater box office statistics for films distributed by the Administrative Institution National Film and Audiovisual Culture Center. The data is up to the last Sunday before the announcement date and does not include films that have not been screened for less than 7 calendar days. The earliest CSV format data in this dataset begins on July 30, 2018, and the earliest JSON format data begins on March 1, 2020. JSON format queries require entering the start and end dates (in the format of year, month, and day), and can provide data for a maximum of 90 days at a time.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes (institutional)

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building, usually live together, who eat from one kitchen or organize daily needs together as one unit. - Group quarters: A special household includes people living in dormitories, barracks, or institutions in which daily needs are under the responsibility of a foundation or other organization. Also includes groups of people in lodging houses or buildings, where the total number of lodgers is ten or more.

Universe

All population residing in the geographic area of Indonesia regardless of residence status. Diplomats and their families residing in Indonesia were excluded.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Indonesia

SAMPLE DESIGN: Geographically stratified systematic sample (drawn by MPC).

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 20,112,539

Mode of data collection

Face-to-face [f2f]

Research instrument

L1 questionnaire for buildings and households; L2 questionnaire for permanent residents; and L3 questionnaire for non-permanent residents (boat people, homeless persons, etc).