The Worldwide Bureaucracy Indicators (WWBI) database is a unique cross-national dataset on public sector employment and wages that aims to fill an information gap, thereby helping researchers, development practitioners, and policymakers gain a better understanding of the personnel dimensions of state capability, the footprint of the public sector within the overall labor market, and the fiscal implications of the public sector wage bill. The dataset is derived from administrative data and household surveys, thereby complementing existing, expert perception-based approaches.

The WWBI includes 302 indicators that are estimated from microdata drawn from the labor force and household welfare surveys and augmented with administrative data for 202 economies in five categories: the demographics of the private and public sector workforces; public sector wage premiums; relative wages and pay compression ratios, gender pay gaps; and the public sector wage bill. The micro and administrative data utilized in the construction of the WWBI are drawn from data catalogs housing surveys conducted by national statistical organizations (NSO) or multilateral organization data teams. Together, these provide an important, albeit narrow, picture of the skills and incentives of bureaucrats. Indicators on public employment track key demographic characteristics including the size of the public sector workforce (in absolute and relative numbers), their age, and distributions across genders, industries, occupations, income quintiles, and academic qualifications. Variables on compensation capture both the competitiveness of public sector wages (compared to the private sector) as well as wage differentials across industry or occupation of employment, genders, education, and income quintiles within the public and private sectors as well as pay compression ratios in public and private sectors. The indicators on the size of the wage bill offer a glimpse into the structure and affordability of the public sector within the larger economy.

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

The South African Gov-ZA multilingual corpus
==============================
Github: https://github.com/dsfsi/gov-za-multilingual
Zenodo: 

About Dataset
---------------------
The data set contains cabinet statements from the South African government. Data was scraped from the governments website:
https://www.gov.za/cabinet-statements

The datasets contain government cabinet statements in 11 languages, namely:

| Language | Code | Language | Code |
|------------|------|------------|------|
| English  | (eng) | Sepedi   | (nso) |
| Afrikaans | (afr) | Setswana  | (tsn) |
| isiNdebele | (nbl) | Siswati  | (ssw) |
| isiXhosa  | (xho) | Tshivenda | (ven) |
| isiZulu  | (zul) | Xitstonga | (tso) |
| Sesotho  | (sot) |


The dataset contains the full data in a JSON file (/data/govza-cabinet-statements.json), as well as CSV’s split by each language, eg: “govza-cabinet-statements-en.csv” for english.
The dataset does not contain special characters like unicode or ascii.

Please see the [data-statement.md](/data_statement.md) for full dataset information. *(TODO)*

Number of Aligned Pairs with Cosine Similarity Score >= 0.65
------------------------------------------------------------

| src_lang | trg_lang | num_aligned_pairs |
|----------|----------|-------------------|
| afr   | eng   | 14549       |
| afr   | nbl   | 6621       |
| afr   | nso   | 15388       |
| afr   | sot   | 8834       |
| afr   | ssw   | 15610       |
| afr   | tsn   | 12605       |
| afr   | tso   | 14936       |
| afr   | ven   | 5776       |
| afr   | xho   | 16065       |
| afr   | zul   | 14998       |
| nbl   | eng   | 3616       |
| nbl   | nso   | 6342       |
| nbl   | sot   | 16163       |
| nbl   | ssw   | 4655       |
| nbl   | tsn   | 3369       |
| nbl   | tso   | 4465       |
| nbl   | ven   | 18984       |
| nbl   | xho   | 5213       |
| nbl   | zul   | 3868       |
| nso   | eng   | 15257       |
| nso   | ssw   | 18697       |
| nso   | tsn   | 16179       |
| nso   | tso   | 17617       |
| nso   | ven   | 6367       |
| sot   | eng   | 5212       |
| sot   | nso   | 8077       |
| sot   | ssw   | 5811       |
| sot   | tsn   | 5450       |
| sot   | tso   | 6586       |
| sot   | ven   | 14098       |
| ssw   | eng   | 15721       |
| ssw   | tso   | 17880       |
| ssw   | ven   | 4588       |
| tsn   | eng   | 14544       |
| tsn   | ssw   | 16386       |
| tsn   | tso   | 16681       |
| tsn   | ven   | 3267       |
| tso   | eng   | 16068       |
| ven   | eng   | 3670       |
| ven   | tso   | 4578       |
| xho   | eng   | 16537       |
| xho   | nso   | 18110       |
| xho   | sot   | 7489       |
| xho   | ssw   | 18387       |
| xho   | tsn   | 16571       |
| xho   | tso   | 17954       |
| xho   | ven   | 4559       |
| xho   | zul   | 18145       |
| zul   | eng   | 16149       |
| zul   | nso   | 17630       |
| zul   | sot   | 5975       |
| zul   | ssw   | 18563       |
| zul   | tsn   | 16482       |
| zul   | tso   | 17789       |
| zul   | ven   | 3606       |


Authors
-------
- Vukosi Marivate - [@vukosi](https://twitter.com/vukosi)
- Matimba Shingange
- Richard Lastrucci
- Isheanesu Joseph Dzingirai
- Jenalea Rajab

Publications
-------

> @inproceedings{lastrucci-etal-2023-preparing,
title = "Preparing the Vuk{'}uzenzele and {ZA}-gov-multilingual {S}outh {A}frican multilingual corpora",
author = "Richard Lastrucci and Isheanesu Dzingirai and Jenalea Rajab and Andani Madodonga and Matimba Shingange and Daniel Njini and Vukosi Marivate",
booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.rail-1.3",
pages = "18--25"
}

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

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

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.

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 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

Abstract

The AfDB's Africa Infrastructure Knowledge Program

The Africa Infrastructure Knowledge Program (AIKP) is a successor program to the Africa Infrastructure Country Diagnostic (AICD) which grew out of the pledge by the G8 Summit of 2005 at Gleneagles to increase substantially ODA assistance to Africa, particularly the infrastructure sector, and the subsequent formation of the Infrastructure Consortium for Africa (ICA). This was against the background that sub-Saharan Africa (SSA) suffers from a weak basic infrastructure base, and that this was a key factor in the SSA region not realizing its full potential for economic growth, international trade, and poverty reduction.

Since 2010, the African Development Bank (AfDB) has taken over leadership for managing the infrastructure database and knowledge work under its Africa Infrastructure Knowledge Program (AIKP). The AIKP builds on the AICD but has a longer-term perspective to provide a platform for: (i) regular updating of the infrastructure database on African countries; (ii) defining and developing analytic knowledge products to guide policy and funding decisions and to inform development policy and program management activities; and (iii) building infrastructure statistical capacity in the region. The AIKP is therefore intended to provide a sustainable framework for generating reliable and timely data on the various infrastructure sectors to guide policy design, monitoring and evaluation and to improve efficiency and delivery of infrastructure services.

The aikp collect a comprehensive data on the infrastructure sectors in Africa-covering power, transport, irrigation, water and sanitation, and information and communication technology (ICT), also the institutional and fiscal issues that cut across infrastructure performance and spending. The institutional issues relate to national level reforms and regulations as well as provider level governance structures in the utility infrastructure sector (energy, water, telecommunications), while the fiscal issues relate to spending and financing of infrastructure.

Geographic coverage

All African Countries

Analysis unit

Pays

Kind of data

Données administratives [adm]

Mode of data collection

Interview de groupe [foc]

Research instrument

Data collection is organized around a series of data templates that are made available for download online or distributed by the Statistical Department of the African Development Bank (AfDB-SD). these templates are organised by sector: Fiscal template: - Fiscal Data Template A: Jurisdictional responsibilities in infrastructure service delivery -national level - Fiscal Data Template B: Special funds financing infrastructure service delivery -national level - Fiscal Data Template C: Basic Budgetary Institutions -national level - Fiscal Data Template D: Budget Cycle, national level - Fiscal Data Template E. Macroeconomic parameters for budgetary context of infrastructure spending - Fiscal Data Template F. Functional and economic classification of government expenses - Fiscal Data Template G. Financial data of public operators Institutional template: - Institutional Data Template A: Reform variables - national level - Institutional Data Template B: Regulation variables - national level - Institutional Data Template C: Governance variables - utility level Power template: - Power Data Template A: National Level Institutions - Power Data Template B: National Level Data Variables - Power Data Template C: Utility Level Data Variables WSS template: - WSS Data Template A: National Level Institutions - WSS Data Template B: Utility Level Data Variables ICT template: - ICT Data Template A: National Level Institutions - ICT Data Template B: National Level Data Variables - ICT Data Template C: National Level Data Variables - ICT Data Template D: Utility Level Data Variables - ICT Data Template E: Operator level - Main national fixed line service provider - ICT Data Template F: Operator level - Largest mobile operator - ICT Data Template G: Operator level - Largest Internet Service Provider Roads template: - Roads Data Template A: Institutional variables – national level - Roads Data Template B: Technical variables – link by link Rails template: - Railways Data template A: Integrated national railway - Railway Data template B: Rail infrastructure company - Railway Data template C: Train operating company - Data template D: Binational railway - Data template E: Dedicated minerals railway Ports template: - Ports Data Template A: Institutional variables - national level - Ports Data Template B: Data variables - ports level Air template: Air Transport Template A: Collection from CAA or Main International Airport

The Mongolia Multiple Indicator Survey was conducted by the National Statistics Office (NSO) with the support and assistance from the Government of Mongolia and UNICEF. The survey was conducted to monitor the progress towards the goals and targets of the United Nations Millennium Declaration, adopted by all 191 United Nations Member States, and the Plan of Action of “A World Fit For Children”, adopted by 189 Member States at the United Nations Special Session on Children in May 2002. The Government of Mongolia has adopted in 2002 "National Program to Improve Child Development and Protection" which ended in 2010. To evaluate the implementation of the Program the Government needed to collect updated information on the children’s situation in Mongolia. The objective of the Multiple Indicator Cluster Survey (MICS) 2010 survey is to collect data on the health, education, development and protection, implementation of rights of children and women in Mongolia, examine females and males knowledge and sexual behavior on HIV, AIDS and to monitor the progress on achieving the goals of the Plan of Action of "A World Fit For Children", Millennium Development Goals and the "National Program to Improve Child Development and Protection".

The Integrated Household Survey (IHS) is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO; http://www.nsomalawi.mw/) 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), Sustainable Development Goals (SDGs) as well as the goals listed as part of the Malawi Growth and Development Strategy (MGDS). Previous rounds of the IHS program have been implemented every 6-7 years, but starting with the IHS4 2016/17 round of data collection, the upcoming IHS rounds will be fielded every 3 years as in line with the NSO vision of collecting poverty data on a more frequent basis.

Official website

Microdata

Abstract

The Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey 2002-2003 is one of the biggest national surveys carried out in accordance with an international methodology with technical and financial support from the World Bank and United Nations Development Programme.

Background This survey was developed in response to provide the picture of the current situation of poverty in Mongolia in relation to social and economic indicators and contribute toward implementation and progress on National Millennium Development Goals articulated in the National Millennium Development Report and monitoring of the Economic Growth Support and Poverty Reduction Strategy, as well as toward developing and designing future policies and actions. Also, the survey enriched the national database on poverty and contributed in improving the professional capacity of experts and professionals of the National Statistical Office of Mongolia.

Purpose Since the onset of the transition to a market economy of Mongolia our country the need to study changes in people's living standards in relation to household members' demographic situation, their education, health, employment and household engagement in private enterprises has become extremely important. With that purpose and with the support of the World Bank and the United Nations Development Programme, the National Statistical Office of Mongolia conducted the Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey-like features between 2002 and 2003. In conjunction with LSMS household interviews the NSO also collected a price and a community questionnaire in each selected soum. The latter collected information on the quality of infrastructure, and basic education and health services.

Main importance of the survey is to provide policy makers and decision makers with realistic information about poverty and will become a resource for experts and researchers who are interested in studying poverty as well as social and economic issues of Mongolia.

In July 2003 the Government of Mongolia completed the Economic Growth and Poverty Reduction Strategy Paper in which the Government gave high priority to the fight against poverty. As part of that commitment this paper is a study that intends to monitor poverty and understand its main causes in order to provide policy-makers with useful information to improve pro-poor policies.

Content The Integrated HIES with LSMS design has the peculiarity of being a sub-sample of a larger survey, namely the Household Income and Expenditure Survey 2002. Instead of administering an independent consumption module, the Integrated HIES with LSMS 2002-2003 depends on the HIES 2002 information on household consumption expenditure. This is why the survey is referred as Integrated HIES with LSMS 2002-2003. This survey is the only source of information of income-poverty, and the questionnaire is designed to provide poverty estimates and a set of useful social indicators that can monitor more in general human development, as well as more specific issues on key sectors, such as health, education, and energy. And, the price and social survey, in conjunction with LSMS household interviews, collected information on the quality of infrastructure, and basic education and health services of each selected soum.

HIES - food expenditure and consumption, non-food expenditure, other expense, income LSMS - general information, household roster, housing, education, employment, health, fertility, migration, agriculture, livestock, non-farm enterprises, other souces of income, savings and loans, remittances, durable goods, energy PRICE SURVEY - prices of household consumer goods and services SOCIAL SURVEY - population and households, economy and infrastructure, education, health, agriculture and livestock, and non-agricultural business

Survey results The final report of this survey has main results on key poverty indicators, used internationally, as they relate to various social sectors. Its annexes contain information regarding the consumption structure, poverty lines along with the methodology used, as well as some statistical indicators.

The main contributions of this survey report are: - new poverty estimates based on the latest available household survey, the Integrated HIES with LSMS 2002-2003 - the implementation of appropriate, and internationally accepted, methodologies in the calculation of poverty and its analysis (these methodologies may constitute a reference for the analysis of future surveys) - a 'poverty profile' that describes the main characteristics of poverty

The first section of the report provides information on the Mongolian economic background, and presents the basic poverty measures that are linked to the economic performance to offer an indication of what happened to poverty and inequality in recent years. A second section goes in much more detail in generating and describing the poverty profile, in particular looking at the geographical distribution of poverty, poverty and its correlation with household demographic characteristics, characteristics of the household head, employment, and assets. A final section looks at poverty and social sectors and investigates various aspects of education, health and safety nets. The report contains also a number of useful, but more technical appendixes with information about the HIES-LSMS 2002-2003 (sample design and data quality), on the methodology used to construct the basic welfare indicator, and set the poverty line, some sensitivity analysis, and additional statistical information.

Geographic coverage

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

Analysis unit

• Household (defined as a group of persons who usually live and eat together)
• Household member (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)
• Selected soums (for collecting prices of household consumer goods and services and information on quality of infrastructure, basic education, health services and so on)

Universe

The survey covered selected households and all members of the households (usual residents). And the price and social surveys covered all selected soums.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated HIES with LSMS 2002-2003 households are a subset of the household interviewed for the HIES 2002. One third of the HIES 2002 households were contacted again and interviewed on the LSMS topics. The subset was equally distributed among the four quarters.

The HIES 2002, and consequently the Integrated HIES with LSMS 2002-2003, used the 2000 Census as sample frame. 1,248 enumerations areas were part of the sample, which is a two-stage stratified random sample. The strata, or domains of estimation, are four: Ulaanbaatar, Aimag capitals and small towns, Soum centres, and Countryside. At a first stage a number of Primary Sampling Units (PSUs) were selected from each stratum. In the selected PSUs enumerators listed all the households residing in the area, and in a second stage households were randomly selected from the list of households identified in that PSU (10 households were selected in urban areas and 8 households in rural areas).

It should be noted that non-response case of households once selected for the survey exerts unfavorable influence on the representativeness of the survey. Therefore an enumerator should take every step to avoid that. To obtain true and timely survey results a proper agreement should be reached with a selected household before a survey starts. One of the main reasons of non-response is that an enumerator doesn't meet with the household members who are able to give the required information. An enumerator should visit a household at least 3 times within the given period to take the questionnaire.

Another common reason is that a household refuses to participate in the survey. In this case an enumerator should explain the purpose of the survey again, explain that the private data will be kept strictly confidential according to the corresponding law. If necessary an enumerator can ask local statistical division or local administration for the help. However this practice is very seldom.

If there is no possibility to take the questionnaires from the selected households due to weather conditions or disasters, reserved households with numbers 11, 12, 13 respectively from the list provided by the NSO should replace the omitted ones. However the reasons of replacements are to be declared in detail on the form.

Sampling deviation

At the planning stage the time lag between the HIES and LSMS interviews was expected to be relatively short. However, for various reasons it is on average of about 9 months, and for some households more than one year. Households interviewed in the first and second quarter of 2002 were generally re-interviewed in March and April 2003, while households of the third and fourth quarter of 2002 were re-interviewed in May, June and July of 2003. The considerable time lag between HIES and LSMS interviews was the main responsible for a considerable loss of households in the LSMS sample, households that could not be easily relocated and therefore re-interviewed. Due also to some incomplete questionnaires, the number of households that were used for the final poverty analysis is 3,308.

Mode of data collection

Face-to-face [f2f]

Research instrument

A

Quarterly levels for UK gross domestic product (GDP) expenditure components, in chained volume measures at market prices.

ข้อมูลระดับย่อย (Micro data) หมายความว่า ข้อมูลเฉพาะบุคคล หรือเฉพาะราย ซึ่งได้มาโดยวิธีการตามพระราชบัญญัติสถิติ พ.ศ. 2550 ทั้งหมดที่ผ่านการตรวจสอบความถูกต้อง ความครบถ้วน และความแนบนัยของข้อมุลเรียบร้อยแล้ว พร้อมที่จะนำไปประมวลผลเป็นสถิติต่อไป

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. The survey has been a joint endeavor of the Government of Mongolia and UNICEF to make an in-depth analysis of Mongolia's child and women health, education, livelihood status and right exercises and to assess the progress of implementation of a National Programme for Child Development and Protection (2002-2010). The data will furnish the preparation process of the national reporting to be presented by the Government of Mongolia at the special session of UN regarding the country's implementation of Declaration of the A World Fit for Children.

Survey Objectives The primary objectives of “Multiple Indicator Cluster Survey: Child Development 2005-2006” are the following: - To update the data for assessing the situation of child and women and their right exercises - To furnish the data needed for monitoring progress towards the goals of Millennium Declaration and the WorldFit for Children as a basis for future action planning - To contribute to the improvement of data and monitoring systems in Mongolia and strengthen the expertise in the design, implementation and analytical of these systems.

Survey plans The Mongolia Multiple Indicator Cluster Survey was conducted by the National Statistical Office of Mongolia with the support of the Government of Mongolia and UNICEF. Technical assistance and training for the surveys was 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 is nationally representative and covers the whole of Mongolia.

Analysis unit

• 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);

• Women aged 15-49

• Children aged 0-4

Universe

The survey covered all 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 principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV/AIDS. The population covered by the MICS - 3 is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child.

The MICS - 3 collected data from a nationally representative sample of households, women and children. The primary focus of the MICS - 3 was to provide estimates of key population and health, education, child protection and HIV related indicators for Mongolia as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 5 regions for key indicators. Mongolia is divided into 5 regions. Each region is subdivided into provinces (aimags) and a capital city, and each province into soums, a capital city into districts, each soum into bags and each districts into khoroos. As bag and khoroo household and population listing is annually updated, these were taken as primary sampling units. Bags and khoroos with a large population were divided into 2-3 primary sampling units in order to keep the similar number of households for sampling units. Bag and khoroos (primary sampling unit) were selected with probability proportional to size and 25 households within each of these selected units were sampled using the systematic method. The primary sampling unit variable is the cluster (HH1).

The survey estimates the indicators on the child and women situation by national level, rural, urban areas and regions. Five regions (Western, Khangai, Central, Eastern and Ulaanbaatar) were the main sampling domains and a two stage sampling design was used. Within each region households were selected with probability proportional to size.

A total of 6325 households in 253 primary sampling units were selected to represent 21 aimags and Ulaanbaatar city. Sample weights were used for estimating the data collected from each of the sampled households. No replacement of households was permitted in case of non-response or non-contactable households. Adjustments were made to the sampling weights to correct for non-response, according to MICS standard procedures.

Sampling deviation

No major deviations from the original sample design were made. All primary sampling units were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the MICS were structured questionnaires based on the MICS - 3 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's characteristics, household listing, education, water and sanitation, child labour, child discipline, child disability, and salt iodization.

To reflect the country specific characteristics, module “Salt Iodization” of household questionnaire was enlarged by the question about the vitamin enriched flour and module “child discipline” was added with sub-module child behaviour. These additions were made based on the decisions made by work group members and Steering Committee.

In the meantime, the salt used for household cooking was on site tested to measure the iodine content.

Household questionnaire was administered to an adult household member who can best represent other members, women questionnaire to women themselves and under-five questionnaire to mothers or caretakers of children under 5 years. Child weights and heights were measured during the interviews.

The women's questionnaire includes women's characteristics, women listing, child mortality, maternal and infant health, marriage, contraception, attitudes towards family violence, and HIV/AIDS knowledge.

The children's questionnaire includes children's characteristics, child listing, birth registration and pre-schooling, child development , “A” vitamin supplement, breastfeeding, care of illness, immunization, and anthropometry.

The questionnaires were developed in Mongolian from the MICS3 Model Questionnaires, and were translated into English.

In order to check the clarity and logical sequence of questions and determine the interview duration per household, the pretest of questionnaires was made in September 2005 covering the selected households in Erdene soum of Tuv aimag. Based on the findings of the pretest, wording and logical sequence of the questions were improved.

Cleaning operations

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

Details of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines in the MICS manual http://www.childinfo.org/mics/mics3/manual.php

Data entry was conducted by 8 data entry operators in tow shifts, supervised by 1 data entry supervisors, using a total of 9 computers (8 data entry computers plus one supervisor's computer). All data entry was conducted at the NSO using manual data entry. 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

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.

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.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

โครงการสำรวจการมีการใช้เทคโนโลยีสารสนเทศและการสื่อสารในครัวเรือน จัดทำขึ้นเพื่อ (1) ทราบจำนวนประชาชนที่ใช้คอมพิวเตอร์และอินเทอร์เน็ต (2) ทราบจำนวนครัวเรือนที่มีอุปกรณ์เทคโนโลยีสารสนเทศและการสื่อสารในแต่ละประเภทต่าง ๆ คือ เครื่องโทรศัพท์พื้นฐานเครื่องคอมพิวเตอร์และการเชื่อมต่ออินเทอร์เน็ตในครัวเรือน (3) ทราบการใช้คอมพิวเตอร์ทักษะการใช้การใช้อินเทอร์เน็ตและรายละเอียดของการใช้ เช่น สถานที่ใช้ กิจกรรมในการใช้ ความถี่ในการใช้ การสั่งซื้อสินค้าหรือบริการ

สำมะโนอุตสาหกรรม หมายถึงการเก็บรวบรวมข้อมูลเกี่ยวกับจำนวนลักษณะและการดำเนินการของสถานประกอบการในอุตสาหกรรมการผลิต ณ สถานที่ตั้งที่แน่นอนทุกหน่วยภายในท้องที่ที่กำหนด

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

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.

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