Malawi NSO Projection: Population: Male data was reported at 22,621,821.000 Person in 2050. This records an increase from the previous number of 22,082,144.000 Person for 2049. Malawi NSO Projection: Population: Male data is updated yearly, averaging 14,437,990.500 Person from Dec 2017 (Median) to 2050, with 34 observations. The data reached an all-time high of 22,621,821.000 Person in 2050 and a record low of 8,830,072.000 Person in 2018. Malawi NSO Projection: Population: Male 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.

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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 3-5 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), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

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 IHS5 sampling frame is based on the listing information and cartography from the 2018 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. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. 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. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Abstract

National Statistical Office (NSO) of India will be conducting Annual Survey on Unincorporated Sector Enterprises (ASUSE) 2023-24 during October 2023 to September 2024.

Objective of Annual Survey of Unincorporated Sector Enterprises (ASUSE) 2023-24

Objective of Annual Survey of Unincorporated Sector Enterprises (ASUSE) is to exclusively measure various economic and operational characteristics of unincorporated non-agricultural establishments pertaining to manufacturing, trade and other services sector (excluding construction). The unit of enquiry of the ASUSE is an ‘establishment’. The main indicators of this survey are various economic characteristics such as, estimated number of establishments, estimated number of workers, GVA per worker, GVA per establishment, emoluments per hired worker, etc. Besides, it also collects information on different types of operational characteristics such as type of ownership, type of location of the establishment, nature of operation, registration status, use of ICT, etc. However, while generating estimates from unit level data, the user should take into account the fact that any study variable created by the user as a combination of two or more operational characteristics (for example, whether the establishment is an NPI and maintaining audited books of accounts) may result in very few sample observations in that domain and hence, can produce estimates which may not be reliable. Besides the study variable, the survey has also gathered auxiliary information on the item 1401 - “Income of the establishment from the entrepreneurial activity(ies) (excluding all kind of agricultural income)” in Block 14. The sole purpose of gathering on this item was to check the internal consistency and validation of the data and not to compile estimates/indicators based on this information. While using the estimates for the Union Territories and smaller States, it may be kept in mind that the sample sizes for them may not be adequate enough for getting sufficiently reliable estimates and interpretation thereof should be made with caution. Similarly, while interpreting the results using estimates at much deeper cross-sectional level (e.g. district level), data users must keep in mind the inadequacy, if any, of the corresponding domain specific samples before arriving at any conclusion. Renting of building for residential purpose was included in coverage of activities in ASUSE 2023-24 under a special NIC code 68108 which was not collected in ASUSE 2022-23. A separate table has been presented in the report providing the estimated number of establishments and workers in different States/UTs engaged in the activity of renting for residential purpose. However, estimates presented in this report in all other tables and statements exclude those establishments.

Comparability of ASUSE 2023-24 with previous ASUSE:

There has been some change in the treatment of "teachers providing tuition" and "individuals serving as housemaids, cooks, gardeners, governesses, babysitters, chowkidars, night watchmen, etc." in ASUSE 2023-24 in comparison to ASUSE 2022-23 or ASUSE 2021-22. Teachers providing tuitions to students by visiting the households of students in lieu of fixed remuneration was treated as out of coverage in earlier ASUSE (2021-22 and 2022-23). However, in ASUSE 2023-24, they were considered within coverage. Similarly, the individuals serving as housemaids, cooks, gardeners, governess, babysitters, chowkidars, night watchman, etc. to a number of households/establishments for activities like grooming of the floor, dusting, cleaning of utensils were treated as self-employed and were covered in ASUSE 2023-24 but not in ASUSE 2021-22 and ASUSE 2022-23. Users may take due cognizance of this fact while using the data for comparison purposes.

Geographic coverage

The survey will cover the rural and urban areas of whole of India (except the villages in Andaman and Nicobar Islands which are difficult to access). The definitions of urban and rural areas as per census 2011 are given below:

Urban: Constituents of urban area are Statutory Towns, Census Towns and Outgrowths.

Statutory Town (ST): All places with a municipality, corporation, cantonment board or notified towns area committee, etc.

Census Town (CT): Places that satisfy the following criteria are termed as Census Towns (CTs). a. A minimum population of 5000 b. At least 75% of the male main working population engaged in non-agricultural pursuits c. A density of population of at least 400 per sq.km.

Out Growth (OG): Out Growth should be a viable unit such as a village or part of a village contiguous to a statutory town and possess the urban features in terms of infrastructure and amenities such as pucca roads, electricity, taps, drainage system, education institutions, post offices, medical facilities, banks, etc. Examples of OGs are Railway colonies, University campuses, Port areas, that may come up near a CT or statutory towns outside its statutory limits but within the revenue limit of a village or villages contiguous to the town or city.

Urban Agglomeration (UA): It is a continuous urban spread constituting a town and its adjoining urban outgrowths (OGs) or two or more physically contiguous towns together and any adjoining urban out-growth of such towns.

Rural: All area other than urban are rural. The basic unit for rural area is the revenue village.

Sampling procedure

Outline of sample design: A stratified multi-stage sampling design will be adopted for ASUSE.

Rural sector: The first stage units (FSU) will be the census villages in the rural sector. For rural part of Kerala, Panchayat wards (PW) will be taken as FSUs.

Urban sector: The First Stage Units (FSU) will be the latest updated UFS (Urban Frame Survey) blocks.

The Ultimate Stage Units (USU) will be establishments for both the sectors. In the case of large FSUs, one intermediate stage of sampling will be the selection of three hamlet-groups (HGs)/sub-blocks (SBs) from each of the large FSUs.

Sampling frame to be used for selection of FSUs

Census 2011 list of villages will be used as the sampling frame for rural areas. Auxiliary information such as number of workers, etc. available from Sixth Economic Census (EC) frame will be used for stratification, sub-stratification and selection of FSUs, for rural areas (except Kerala). In rural areas of Kerala, list of Panchayat Wards (PW) as per Census 2011 will be used as sampling frame. For all urban areas, the latest updated list of UFS blocks will be the sampling frame.

Stratification of FSUs:

Rural sector: Each NSS State region will constitute a rural stratum.

Urban sector: In urban areas, strata will be formed within each NSS State region on the basis of population of towns as per Census 2011. The tentative stratum numbers and their composition (within each NSS State region) will be as follows:

stratum 1 : all towns with population less than 50,000 stratum 2 : all towns with population 50,000 or more but less than 3 lakhs stratum 3 : all towns with population 3 lakhs or more but less than 10 lakhs stratum 4, 5, 6, ... : each city with population 10 lakhs or more

Mode of data collection

Face-to-face [f2f]

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

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.

Sampling deviation

The total sample size for the IHS3 was 12,288 households sampled from a total of 768 EAs. At the end of the survey, a total of 12,271 households were interviewed. Of the 12,271 interviewed households, 688 were replacements (6 percent)

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Entry:

Data Entry Clerks Each IHS3 field team was assigned 1 data entry clerk to process completed questionnaires at the team's 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 (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 catalogued by the Data Manager and electronic data loaded onto a central server to enable data entry verification on networked computers.

Quality Checks:

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 catalogued 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 re-entered. 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.

Response rate

99.9 percent

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2006 Thailand Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Thailand; - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals (MDG), the goals of A World Fit for Children (WFFC) and other internationally agreed upon goals, as a basis for future action at national and provincial level; and - To contribute to the improvement of data and monitoring systems on the situation of children and women in Thailand and strengthening technical expertise for the design, implementation, and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was implemented by the National Statistical Office of Thailand, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey was designed to produce estimates for indicators at the national level, by urban and rural disaggregation, for each of the 4 regions of Thailand (North, Northeast, Central, and South) and by individual province for 26 (out of 76 total) targeted provinces (note: additional data collections were performed for the targeted provinces during March-May 2006; separate results publications for each province are pending).

Analysis unit

• Households
• Women aged 15-49
• 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

The Thailand Multiple Indicator Cluster Survey (MICS) was carried out by a sample survey method that used a stratified two stage sampling plan. The primary sample units (PSUs) consisted of blocks (in municipal areas) or villages (in non-municipal areas). The secondary sample units consisted of collective households systematically drawn from a household listing. The plan is designed to provide estimates of situation indicators for children and women at the national level, for municipal and non-municipal areas, and for four regions: Central (including Bangkok), North, Northeast and South. The household listing is obtained from The Basic Household Information Survey conducted every two years by the National Statistical Office (NSO). In the survey, members of each household located in the block/village samples are counted.

Data on basic household information from the survey are to be used as the sample frame in various survey projects of the NSO. Data from the 2006 Basic Household Information Survey were used as the frame for household samples in the Thailand MICS. Thirty collective household samples per block/village sample were selected in both municipal and non-municipal areas. Field staff then created a Listing of Household Samples by adding together all the names of household heads and the addresses. After a household listing was carried out within the selected 30 households in each block/village, a systematic sample of households was drawn. For national-level results, sample data were weighted in accordance with sampling plan.

A block is an operational boundary in a municipal area that is made up of approximately 100 to 200 households. Blocks are established on a map so that field staff know the exact area they are to cover in the survey.

A village is an administrative unit, a community, in a non-municipal area governed by a village head (Phuyaiban) or a district head (Kamnan).

The MICS national-level report included 1,449 block/village samples. Thirty collective household samples per block/village samples were selected and a total of 43,470 household samples were obtained.

For MICS provincial-level reports, 1,032 block/village samples were selected and 30,960 household samples were included.

More detailed information on the sample design is available in Appendix A of the Survey Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Thailand MICS were structured questionnaires based on the MICS3 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.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child.

The questionnaires were translated into Thai by the NSO MICS coordinators in September 2005.

In addition to the administration of questionnaires, fieldwork teams tested salt used for cooking in the households surveyed for presence of iodine, and measured the weight and height of children under 5 years of age.

Cleaning operations

After the fieldwork, the team supervisor checked the data collected during the interview for completeness. Then the Provincial Statistical Officer in each province and the Director of the Data Management Division of the Bangkok Metropolitan Administration randomly rechecked the data before sending all the questionnaires to the National Statistical Office (NSO) for processing.

Upon receiving the questionnaires from the 76 provinces, the collected data were entered on 30 microcomputers by data entry operators and data entry supervisors at the Thai NSO, using CSPro software. In order to ensure quality control, editing and structural checks, all questionnaires were double entered for verification and internal consistency checks were performed, followed by secondary editing. The data entry and verification used CSPro programme applications that were developed under the global Multiple Indicator Cluster Survey (MICS) project by UNICEF to be used as standard processing procedures worldwide. In Thailand, the standard CSPro programme was modified appropriately to the Thai version questionnaires. The modification was done by NSO staff that had been trained on data processing by MICS experts from UNICEF.

Data entry and data verification for the national level report began in February 2006 and was completed in April 2006. For the provincial reports, the process was completed in June 2006. Data were analysed using the Statistical Package for Social Sciences (SPSS) software programme, Version 14, and the model syntax and tabulation plans developed by UNICEF for this purpose.

Data processing used the CSPro programme applications developed under the global Multiple Indicator Cluster Survey project by UNICEF.

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that

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), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

The IHS1 was conducted in Malawi from November 1997 through October 1998 and provided for a broad set of applications on policy issues regarding households' behaviour and welfare, distribution of income, employment, health and education. The Second Integrated Household Survey was implemented with technical assistance from the World Bank in order to compare the current situation with the situation in 1997-98, and to collect more detailed information in specific areas. The IHS2 fieldwork took placed from March 2004 through February 2005. The Third Integrated Household Survey (IHS3) expanded on the agricultural content of the IHS2 and was implemented from March 2010 to March 2011 under the umbrella of the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative, whose primary objective is to provide financial and technical support to governments in sub-Saharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys with a strong focus on agriculture. The Fourth Integrated Household Survey 2016-2017 (IHS4) which was implemented in the period of April 2016-April 2017 covering 780 EAs throughout Malawi. As part of this project NSO also implemented the Integrated Household Panel Survey 2016 as a follow up to the IHPS 2013.

Geographic coverage

National

Analysis unit

Households

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 IHS4 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. This is the first round of the survey to include the island district of Likoma in the sampling frame. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS4 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS4. Note: Detailed sample design information is presented in the "Fourth Integrated Household Survey 2016-2017, Basic Information Document" document.

Sampling deviation

The total sample size for the IHS4 was 12,480 households sampled from a total of 779 EAs5. At the end of the survey, a total of 12,447 households were interviewed. The survey allowed replacement of households. Of the 12,447 interviewed households, 557 were replacements (4.5 percent).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS4 was implemented using the World Bank's Survey Solutions CAPI software. To carry out IHS4, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8-inch GPS-enabled Samsung Galaxy Tab S2 tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar - checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

DATA MANAGEMENT The IHS4 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS4 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in IHS3 and IHPS. 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. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS4 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

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 errors generated with the Survey Solutions application. For questions that flagged an error, enumerators were expected to record a comment within the questionnaire to explain to their Supervisor the reason for the error and confirming that they double checked the response with the respondent. Supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some Supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the Supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field and this resulted from the additional error reports generated in STATA and sent to teams via email. Field supervisors collected reports for their assignments 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 in the rejected questionnaires and sent back to headquarters.

Additional cleaning was performed after interviews were “Approved” 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 IHS4.

Response rate

99.7 percent

Abstract

The HSES 2010 is a nationally representative survey, which aims to evaluate and monitor the income and expenditure of households, update the basket and weights for consumer price index, and offer inputs to the national accounts. The HSES is a survey regularly conducted by the NSO and covers a 12-month period for analysis.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

Mongolia is divided into 21 aimags. Ulaanbaatar is the capital city and is subdivided into 9 districts, 121 khoroos and 1,035 khesegs. Each kheseg has approximately 200 households. The rest of the country is divided into soums and bags. One of the soums in each aimag is normatively considered as the aimag center, while the others are regarded as the rural area.

Universe

• Households (defined as a group of persons who usually live and eat together)
• Household members (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2010 HSES used the sampling frame which was developed by the NSO based on 2005 population figures obtained from local registration offices. This updated sampling frame was of great importance because the spatial distribution of the population had changed dramatically over the last years and any frame based on the Census 2000 would not be relevant anymore.

The design of the survey recognizes three explicit strata: Ulaanbaatar, aimag centers, and soum centers and the countryside. In addition, the sample was implicitly allocated by districts and khoroos in Ulaanbaatar, and by aimags in rural areas. Each aimag center was an explicit sub-stratum. The selection strategy was different in each stratum: a two-stage process in urban areas and a three-stage process in rural areas. In Ulaanbaatar, 360 khesegs were initially selected, from each of which 10 households were chosen. In aimag centers, 12 or 24 bags were initially selected, and then 10 households from each bag. In rural areas, first 52 soums, then 12 bags in each soum and finally 8 households in each bag were selected. All 1,248 primary sampling units or clusters (units, bags or soums) were selected with a probability proportional to their sizes and were randomly allocated into twelve months of survey fieldwork. The use of this sampling procedure means that households living in different areas of the country have been selected with different probabilities. Therefore, in order to obtain representative statistics for each stratum and for the country as a whole, it was necessary to use sampling weights. The weight which was assigned to each household corresponds to the inverse of the selection probability and takes the sampling strategy into account.

The sample of 11,232 households was allocated as follows: 3,600 in Ulaanbaatar, 2,640 in aimag centers and 4,992 in rural areas and soum centers. However, the actual sample size used for this analysis is slightly smaller: 3,572 households in Ulaanbaatar; 2,639 in aimag centers; and 4,987 in rural areas and small towns. The difference is explained by 60 households, for which complete information was unavailable and were thus, excluded.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of HSES 2007/08 contains 15 major modules: basic socio-economic information about the members of the household, education, health, reproductive health, migration, employment, wage jobs, job search, agriculture and herding, non-farm family businesses, other income, savings and loans, housing and energy, durable goods, nonfood expenditures and food consumption. Also contains 4 additional modules: purchases of food during the past month for urban households (by recall ), consumption of food and other frequenty purchased commodities for urban households (from diary), purchases of food during the part month for rural households, consumption of food during the past 7 days (by recall ) for rural households.

Cleaning operations

All procedures were streamlined and centralized, which is likely to have had a positive impact on the quality of the information. On the other hand, three different rounds of consistency checks were applied to the data: first during the data entry process, then during the compilation of the raw data files and finally during the preparation of this report. In all cases it was possible to compare these listings against the actual questionnaires filled out by the households (and at least during the first round of checks, some households were visited again) and the data were amended whenever a mistake was found.Databases for the HSES 2007/08 have been unified and data error checking was made (by using STATA program) in cooperation with working group.

Analysis of ‘World Development Report 2021 - Figure 2.9 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0037975/ on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Panel a. NSO independence and statistical performance
Panel b. Freedom of the press and statistical performance
Main sources:
NSO independence score: Mo Ibrahim Foundation, Ibrahim Index of African Governance (database).
World Press Freedom Index: Reporters without Borders, 2020 World Press Freedom Index (database).
Statistical Performance Indicators (SPI).
Statistical Performance Index.

--- Original source retains full ownership of the source dataset ---

The National Statistics Office (Malta) publishes numerous national level statistical datasets on an annual basis. These include, but are not limited to: labour market; education; population; tourism; prices; transport; environment; and agriculture. Data relating to mineral reserves, resources and production do not appear to be reported by the National Statistics Office (Malta).

Website: https://nso.gov.mt/en/Pages/NSO-Home.aspx

Abstract

The National Statistics Office, previously known as the Central Bureau of Statistics, conducted the National Sample Census of Agriculture 2021/22 (NSCA 2021/22) covering all parts of the country. Nepal has a glorious history of taking the agriculture census once every ten years, with the first one taking place in 1961/62 and subsequent ones in 1971/72, 1981/82, 1991/92, 2001/02, 2011/12, and 2021/22. The NSCA 2021/22 is the seventh census in this cycle and the first one after the new federal setup of the country. Its primary purpose is to provide data on the tructural aspects of agriculture that change slowly over time, such as farm size, land use, crop areas, and number of livestock, up to the local level (municipality). The census also includes the basic data on the organizational structure of agricultural holdings, including land tenure, irrigation, livestock numbers, labor, and use of machinery and other agricultural inputs. Furthermore, the census content has been broadened to encompass current areas of concern that vary annually, including the production of major crops. The census provides benchmark data on agriculture which is essential for monitoring and evaluating the impact of development policies and programs and addressing emerging social, economic, and environmental policy issues in agriculture. Regarding the content of the census, including statistical concepts, definitions, classifications, and output, the census has adhered to the guidelines set forth by the World Program for the Census of Agriculture 2020 (WCA 2020) developed by the FAO.

The main objectives of the agriculture census 2021/22 are as following :

1. To provide basic data on the structure of agriculture and characteristics of holdings for small geographical area (municipality),

2. To assist in planning and policy-making for agricultural development across the three tiers of government and monitoring the progress achieved,

3. To provide reliable data for benchmarking and reconciliation of current agriculture statistics,

4. To design frame for other agricultural surveys,

5. To avail core data for compilation and monitoring of some agriculture-related SDG indicators.

Geographic coverage

The seventh census of agriculture 2021/22 also covers the entire country including all districts and local levels (Urban and Rural Municipalities).

Analysis unit

Agriculture Holding

Universe

The census covers individual agriculture holdings of the country.

Kind of data

Census data [cen]

Sampling procedure

Sampling design

1. Domain of estimation Nepal is divided into seven provinces, 77 districts, and 753 municipalities for administrative purposes. The NSCA 2021/22 provides accurate estimates at the municipality level, making the 753 municipalities as domains of estimation for the sampling design.

2 Sampling method The sampling method for estimation of various parameters of interest at municipality level is one of strati?ied two-stage sampling. Within a municipality the enumeration areas (EAs) are the primary stage units (PSUs) of sampling and within the selected enumeration area the agricultural households are the second stage units (SSUs) of sampling. The enumeration areas are selected by probability proportional to size (PPS) systematic sampling (the number of holdings in the enumeration area is the size variable). The SSUs are selected by equal probability systematic sampling with implicit stratification.

3 Sampling frame In line with the proposed sampling design, there are two types of sampling frame used for the agriculture census 2021/22: the frame for selecting the PSUs and the frame for the selection of agricultural holdings. The sampling frame for PSUs was prepared from the list of enumeration areas (EAs) from the National Population and Housing Census 2021 (NPHC 2021). Following FAO recommendations an agricultural module was incorporated in the NPHC collecting basic agriculture related information from all households in the country including total area of operational holding, number of livestock, and number of poultry birds The frame of PSUs consisted of the list of enumeration areas along with the number of households and agricultural households.The frame for SSUs was developed through listing operations in the sampled EAs. All households are interviewed in each EA in order to develop an updated list of agricultural households as sample frame of SSUs in the selected EA.

4 Sample size The municipality is the sample domain of the census, therefore the sample size was determined ensuring reliable estimations of key variables of interest at municipality level. As recommended by FAO, agricultural area is a suitable variable that is considered in calculating the sample size. The target number of holdings sampled from each selected EA was set at 25. The actual number sampled varied between 20 and 30 and was determined in such a way to ensure equal probability of selection for all holdings in a municipality. Altogether, a sample of 330,112 holdings for the whole country (8% of all holdings) were selected from 13,576 EAs in the NSCA 2022.

5 Sample selection

The sample of PSUs was selected with a systematic probability proportional to sizemethod considering the number of agricultural households as measure of size.Selection of SSUs (agricultural households) were carried out in the field. The selection was done by using usual equal probability linear systematic sampling. However, before selection, an implicit stratification for Tarai and Hill/Mountain was used by making four implicit strata as follows: • Less than 1 Bigha (0.68Ha)/10 Ropani (0.51Ha) • 1 to 3 Bigha (0.68 to 2.03 Ha)/10 to 20 Ropani (0.51 to 1.01 Ha) • More than 3 Bigha (2.03 Ha)/ 20 Ropani (1.01 Ha) • Only having livestock.

Sampling deviation

No need to derive sample design

Mode of data collection

Face-to-face f2f

Research instrument

The questionnaires implemented in the National Sample Census of Agriculture 2021/22 to collect data are as follows: 1. Holding listing form (Form 1) Form 1 is a holding listing form that has been used to list all the agriculture holdings (within the cut-off threshold) in the selected enumeration area. It has been used as a frame to select the holdings (SSUs).

2 Selected holding listing form (Form 1A) The Form 1A is used to prepare a list of selected holdings that is used to fill out the main questionnaire (Form 2).

3 Agriculture holding questionnaire (Form 2) Form 2 is the main questionnaire implemented in the census to collect the agricultural data in detail from the selected holdings.

4 Community questionnaire (Form 3) Form 3 is used to collect community-level data from the ward office of the municipality.

Cleaning operations

The completed questionnaires collected from the various census offices were safely stored in the central storage building. Data processing for the census was done within the NSO premises. The data processing center of the NSO was equipped with basic facilities and functionalities like laptops, a local server, a local area network (LAN), security cameras, furniture, and air conditioners.The coding and editing of the questionnaires were accomplished by the temporarily recruited 50 coders and editors from November, 2022 to January, 2023. Likewise, the data entry of the hardcopy questionnaire were accomplished by the temporarily recruited 100 entry operators from November, 2022 to January, 2023.

Response rate

100%

Sampling error estimates

The NSO was highly focused on ensuring the accuracy of census data by implementing various measures to minimize non-sampling errors. To reduce sampling errors, an appropriate sampling design was prepared modifying the designs used in previous agriculture sample censuses. Quality control mechanisms for the data included training, supervision, completeness checks, verification of data entry, and consistency checks.

Data appraisal

Census estimates given in the tables are subject to sampling errors, standard error, relative standard error because the data are based on a sample of holdings rather than the entire population of holdings.The size of the SE,SE, RSR are estimated for major outputs. It is presented seperately in a technical report. The technical report provided more detailed information about how the errors are calculated. Therefore,in interpreting the tables, the figures should be suitably rounded off.

According to a 2024 report, around 23 percent of the Pegasus spyware users worldwide were intelligence agencies. The spyware developed by the Israeli technology company NSO is one of the most sophisticated tools for online surveillance ever created. According to the transparency report published by the company, about 46 percent of its users were reportedly law enforcement entities.

The Third Integrated Household Survey (IHS3) was conducted by the National Statistical Office (NSO) from March 2010 to March 2011. The Survey is a nationally representative sample survey designed to provide information on the various aspects of household welfare in Malawi. The survey collected information from a sample of 12,288 households statistically designed to be representative at both national, district, urban and rural levels enabling the provision of reliable estimates for these levels. The reports, data and meta-data for the IHS 3 can be accessed through this link: http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,contentMDK:23152072~menuPK:4196952~pagePK:64168445~piPK:64168309~theSitePK:3358997,00.html

Abstract

The 2013 NDHS is designed to provide information on fertility, family planning, and health in the country for use by the government in monitoring the progress of its programs on population, family planning and health.

In particular, the 2013 NDHS has the following specific objectives: • Collect data which will allow the estimation of demographic rates, particularly fertility rates and under-five mortality rates by urban-rural residence and region. • Analyze the direct and indirect factors which determine the level and patterns of fertility. • Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. • Collect data on health, immunizations, prenatal and postnatal check-ups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever and acute respiratory infections among children below five years old. • Collect data on environmental health, utilization of health facilities, health care financing, prevalence of common non-communicable and infectious diseases, and membership in the National Health Insurance Program (PhilHealth). • Collect data on awareness of cancer, heart disease, diabetes, dengue fever and tuberculosis. • Determine the knowledge of women about AIDS, and the extent of misconception on HIV transmission and access to HIV testing. • Determine the extent of violence against women.

Geographic coverage

National coverage

Analysis unit

• Household
• Individuals/ persons
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selection methodology for the 2013 NDHS is based on a stratified two-stage sample design, using the 2010 Census of Population and Housing (CPH) as a frame. The first stage involved a systematic selection of 800 sample enumeration areas (EAs) distributed by stratum (region, urban/rural). In the second stage, 20 sample housing units were selected from each sample EA, using systematic random sampling.

All households in the sampled housing units were interviewed. An EA is defined as an area with discern able boundaries consisting of contiguous households. The sample was designed to provide data representative of the country and its 17 administrative regions.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2013 NDHS used three questionnaires: Household Questionnaire, Individual Woman’s Questionnaire, and Women’s Safety Module. The development of these questionnaires resulted from the solicited comments and suggestions during the deliberation in the consultative meetings and separate meetings conducted with the various agencies/organizations namely: PSA-NSO, POPCOM, DOH, FNRI, ICF International, NEDA, PCW, PhilHealth, PIDS, PLCPD, UNFPA, USAID, UPPI, UPSE, and WHO. The three questionnaires were translated from English into six major languages - Tagalog, Cebuano, Ilocano, Bicol, Hiligaynon, and Waray.

The main purpose of the Household Questionnaire was to identify female members of the sample household who were eligible for interview with the Individual Woman’s Questionnaire and the Women’s Safety Module.

The Individual Woman’s Questionnaire was used to collect information from all women aged 15-49 years.

The Women’s Safety Module was used to collect information on domestic violence in the country, its prevalence, severity and frequency from only one selected respondent from among all the eligible women who were identified from the Household Questionnaire.

Cleaning operations

All completed questionnaires and the control forms were returned to the PSA-NSO central office in Manila for data processing, which consisted of manual editing, data entry and verification, and editing of computer-identified errors. An ad-hoc group of thirteen regular employees from the DSSD, the Information Resources Department (IRD), and the Information Technology Operations Division (ITOD) of the NSO was created to work fulltime and oversee data processing operation in the NDHS Data Processing Center that was carried out at the NSO-CVEA Building in Quezon City, Philippines. This group was responsible for the different aspects of NDHS data processing. There were 19 data encoders hired to process the data who underwent training on September 12-13, 2013.

Data entry started on September 16, 2013. The computer package program called Census and Survey Processing System (CSPro) was used for data entry, editing, and verification. Mr. Alexander Izmukhambetov, a data processing specialist from ICF International, spent two weeks at NSO in September 2013 to finalize the data entry program. Data processing was completed on December 6, 2013.

Response rate

For the 2013 NDHS sample, 16,732 households were selected, of which 14,893 were occupied. Of these households, 14,804 were successfully interviewed, yielding a household response rate of 99.4 percent. The household response rates in urban and rural areas are almost identical.

Among the households interviewed, 16,437 women were identified as eligible respondents, and the interviews were completed for 16,155 women, yielding a response rate of 98.3 percent. On the other hand, for the women’s safety module, from a total of 11,373 eligible women, 10,963 were interviewed with privacy, translating to a 96.4 percent response rate. At the individual level, urban and rural response rates showed no difference. The principal reason for non-response among women was the failure to find individuals at home, despite interviewers’ repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2013 National Demographic and Health Survey (NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between the results of all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey data.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2013 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2013 NDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replications method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of weighted cases in the group or subgroup under consideration.

Further details on sampling errors calculation are given in Appendix B of the final report.

Data appraisal

Data quality tables were produced to review the quality of the data: - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: The tables are presented in APPENDIX C of the final report.

Credit report of N S O Group Technologies Ltd contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

This data release includes biomarker ratio values calculated from measurements made at the USGS for the reference oil NSO-1 that were reported in a journal article entitled Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS.

According to a 2024 report, the Pegasus spyware developed by the Israeli technology company NSO had clients in 31 countries worldwide. Overall, the company had 54 customers. According to the same report, nearly half of the company's customers were intelligence agencies.

Data useful for SDG Reporting using DevInfo / PNGInfo. National Statistics Office (NSO) are the Custodians of the Dataset

Abstract

The Integrated Household Survey (IHS) is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office 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).

The First Integrated Household Survey (IHS1) was implemented with technical assistance from the International Food Policy Research Institute (IFPRI) and the World Bank (WB). The IHS1 was conducted in Malawi from November 1997 through October 1998 and provided for a broad set of applications on policy issues regarding households' behaviour and welfare, distribution of income, employment, health and education. The Second Integrated Household Survey was implemented with technical assistance from the World Bank in order to compare the current situation with the situation in 1997-98, and to collect more detailed information in specific areas. The IHS2 fieldwork took placed from March 2004 through February 2005. The Third Integrated Household Survey (IHS3) expanded on the agricultural content of the IHS2 and was implemented from March 2010 to March 2011 under the umbrella of the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative, whose primary objective is to provide financial and technical support to governments in sub-Saharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys with a strong focus on agriculture.

A sub-sample of IHS3 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS) (ii) (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection.

Geographic coverage

National

Analysis unit

Households

Universe

The IHPS attempted to track all baseline households (from the IHS3) as well as individuals that moved away from the baseline dwellings between 2010 and 2013 as long as they were neither servants nor guests at the time of the IHS3; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The IHPS CSPro based data entry application was designed to stream-line the data collection process from the field. Completed data capture for enumerations areas was packaged automatically by the data entry application into a compressed zip file specific to each enumeration area code. These files contained all household level interviews for that enumeration area and were then submitted back to the NSO central office. These files were to be transmitted back on a rolling basis. For IHPS the field teams were each provided an internet dongle and airtime for timely submission of the data files as limited access to internet cafes and file corruption was a notable issue in the IHS3 project.

Once data files were received by the NSO central office, enumeration area files were downloaded and catalogued by date received. Data was compiled and exported into Stata files on a regular basis and weekly reports were generated with assistance from the IHPS World Bank Resident Advisor on the status of data completion. Over-all data collection status reports were relayed to NSO IHPS Managers on a weekly basis. Overdue or incomplete data files were flagged for immediately follow-up.

The IHPS data files received from the field were also downloaded by the IHPS Data Manager and uploaded to the data verification server to await second data entry. To perform second data entry, individual computers would retrieve and load the field data for the specific enumeration area. Once data verification was complete, verified enumeration data files were zipped and uploaded automatically to the server. Daily back-up of the server to a local network computer was conducted at the end of every day and back-ups to remote location weekly.