Link Function: information

Standardized test-fishing with Nordic multi-mesh gillnets is a widely used method in Sweden to investigate the fish fauna in a lake. In 2005 the method became an European standard (EN 14757). Standardized test-fishing catch most of the Swedish species in a representative way, giving a good estimate of species abundance and size distribution. The results are generally used for purposes of environmental protection and fishery management.

NORS consists of thousands of test-fishing occasions back to the 1950’s. The Department of Aquatic Resources (SLU Aqua) at the Swedish University of Agricultural Sciences is responsible of collecting and checking test-fishing data generated in national and regional environmental programs, on behalf of the Swedish Agency for Marine and Water Management. SLU Aqua also collect test-fishing data from several other types of investigations in order to create a database as representative as possible. The purpose is to facilitate obtaining data of high quality for research, national investigations and reports. The database also serves as a reference for local and regional investigations. All data is available for the public.

Link Function: information

The Department of Aquatic Resources (SLU Aqua) at the Swedish University of Agricultural Sciences is responsible of collecting and checking test-fishing data generated in national and regional environmental programs on behalf of the Swedish Agency for Marine and Water Management. The test fishing is performed with standardized methods. SLU Aqua also collect test-fishing data from several other types of investigations (e g recipient monitoring). The purpose is to facilitate obtaining data of high quality for research, national investigations and reports. The database also serves as a reference for local and regional investigations. Data is available for the public on http://www.slu.se/kul.

The Pacific Guide to Statistical Indicators for Human Rights Reporting is the first such document of its kind on human rights reporting under the core human rights treaties in the Pacific. It proposes a set of indicators that are contextually relevant and meaningful for the region, acknowledging the unique challenges and opportunities that Pacific Island countries experience in data collection, national institutions and resources. The guide is meant to assist coordinating bodies within Pacific Island governments responsible for preparing initial and periodic reports on the country’s progress on implementation of ratified treaties, as well as those overseeing the implementation of national human rights action plans. The guide will also help national statistics offices to better understand how the statistical data they collect, analyse and provide feeds into human rights reporting.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Household

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

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

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Indonesia

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

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

The National Household Education Survey (NHES) series reports information on the condition of education in the United States by collecting data at the household level rather than using a traditional school-based data collection system. The surveys attempt to address many current issues in education, such as preprimary education, school safety and discipline, adult education, and activities related to citizenship. This survey included two topical survey components: Parent/Family Involvement in Education (PFI) and Adult and Youth Civic Involvement (CI). The PFI component, which elicited information from parents and children aged 3 years through grade 12, focused on four areas: types and frequency of family involvement in children's schools, communication with teachers or other school personnel, children's homework and behavior, and learning activities with children outside of school. Other information collected for this component pertained to student experiences at school, children's personal and demographic characteristics, household characteristics, and children's health and disability status. The PFI information is provided in Part 1, Parent and Family Involvement in Education and Civic Involvement -- Parent Data. The CI component of the survey gathered information on civic participation, sources of information about government issues, and knowledge and attitudes about government. Items were administered to youths in grades 6 through 12 (Part 2, Youth Civic Involvement Data) and their parents, as well as to a representative sample of United States adults (Part 3, Adult Civic Involvement Data). The CI component also addressed opportunities for youth to develop personal responsibility and skills that would facilitate their taking an active role in civic life. CI questions were also asked of the parents surveyed in the PFI component, and these data also can be found in Part 1. In addition to the two major topical components, a screener component of the survey collected demographic and educational information on all members in every household contacted, regardless of whether anyone in the household was selected for an extended interview. (The term "extended interview" refers to the interviews completed in the topical components of the study, i.e., the Parent PFI/CI, the Youth CI, or the Adult CI interviews.) Items on the use of public libraries by the household were also administered in the screener portion for households without Parent PFI/CI extended interviews and in the first Parent PFI/CI interview in households in which one or more children were sampled. These data are presented in Part 4, Household and Library Data.

General information title of the dataset: "data for the article <

Electrofishing is a quantitative method to monitor the fish fauna in running waters, allowing fish to be released back alive. SERS consists of results from 81 000 electrofishing occasions all over Sweden, as far as back to the 1950’s. The Department of Aquatic Resources (SLU Aqua) at the Swedish University of Agricultural Sciences is responsible of collecting and quality assuring test-fishing data generated in national and regional environmental programs, on behalf of the Swedish Agency for Marine and Water Management. The purpose is to facilitate the access of high quality data for research, national investigations and reports. The database also serves as a reference for local and regional investigations. All data is available for the public (www.slu.se/elfiskeregistret).

Abstract

In an effort to address the ICT data challenges, the Communications Commission of Kenya (CCK) partnered with Kenya National Bureau of Statistics (KNBS) to undertake a comprehensive National ICT Survey. This was planned and executed during the months of May and June 2010.

The main objective of the study was to collect, collate and analyse data relating to ICT access and usage by various categorizations in Kenya. The survey captured data and information on critical ICT indicators as defined by international bodies such as the International Telecommunications Union (ITU). These indicators focused on household and individuals; and the data was be disaggregated by age, gender, administrative regions, rural and urban locations.

The specific objectives of the study were to; Obtain social economic information with a view of understanding usage patterns of ICT services; (a) Obtain social economic information with a view of understanding usage patterns of ICT services; (b) Collect, collate and analyze ICT statistics in line with ICT indicators; (c) Evaluate the factors that will have the greatest impact in ensuring access and usage of ICTs and; (d) Develop a database on access and usage of ICT in Kenya

Geographic coverage

National coverage

Analysis unit

District, Household, Individual

Universe

Households from the sampled areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The National Sample Survey and Evaluation Programme (NASSEP IV) maintained by the Bureau was used as the sampling frame. The frame has 1,800 clusters spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into four domains, namely: 1. National, 2. Major Urban areas, 3. Other Urban areas, and 4. Rural areas.

The major urban towns included Nairobi, Thika, Mombasa, Kisumu, Nakuru and Eldoret. All other areas defined as urban by KNBS but fall outside the major municipalities above were categorized as 'other urban areas'. The rural domain was further sub-divided into their respective provinces, excluding Nairobi which is purely urban. For the 'rural' component, the districts that display identical socio-cultural and economic conditions have been pooled together to create strata from which a representative set of districts is selected to represent the group of such districts. A total of 42 such stratifications were done and one district in each categorization was selected. The major urban areas of the country namely Nairobi, Mombasa, Kisumu, Nakuru, Eldoret and Thika were all sub-stratified into five sub-strata based on perceived levels of income into the: 1. Upper income 2. Lower Upper 3. Middle 4. Lower Middle and 5. Lower.

In this survey, all the six 'major urban' are included while just a few of the 'other urban areas' are selected depending on their population (household) distribution.

Selection of the Clusters for the Survey The selection of the sample clusters was done systematically using the Equal Probability Selection method (EPSEM). Since NASSEP IV was developed using Probability Proportional to Size (PPS) method, the resulting sample retains its properties. The selection was done independently within the districts and the urban /rural sub-stratum.

Selection of the Households From each selected cluster, an equal number of 15 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the office and assigned to the Research Assistants, with strictly no allowance for replacement of non-responding households.

Sampling deviation

Owing to the some logistical challenges the following clusters were partially or not covered at all: • One cluster in Tana River due to floods. • Two clusters in Molo where households shifted to safer areas after the Post Election Violence (PEV). As a result, fewer than the expected households were covered. • One cluster in Koibatek was covered halfway due to relocation of households to pave way for a large plantation.

Where there was no school found within the cluster, Research Assistant was allowed to sample an institution from a neighbouring cluster. In some districts, the schools were found to be very far from the cluster and therefore could not be covered. Where a cluster was to be covered over a weekend, it was often not possible to find a responsible person in institutions to respond to the questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaire: This will be used to collect background information pertaining to the members of the household and businesses operated by household members. It will collect information about each person in the household such as name, sex, age, education, and relationship to household head etcetera. This information is vital for calculating certain socio-demographic characteristics of the household. The Business module in the household questionnaire will be used to collect information pertaining to usage of ICT in businesses identified in the household. To estimate the magnitude, levels and distribution of ICT usage in the country, all the selected respondents 15 years and above will be subjected to business questionnaire.

Institutional Questionnaire: This will collect information pertaining to institutions providing ICT related programmes in the country. This information will be analyzed to identify gaps and other issues of concern, which need to be addressed in the promotion ICT provision in the country.

Cleaning operations

As a matter of procedure initial manual editing was done in the field by the RAs. The supervisors further checked the questionnaires and validated the data in the field by randomly sampling 20 per cent of the filled questionnaires. After the questionnaires were received from the field, an office editing team was constituted to do office editing.

Data was captured using Census and Survey Processing System (CSPRO) version 4.0 through a data entry screen specially created with checks to ensure accuracy during data entry. All questionnaires were double entered to ensure data quality. Erroneous entries and potential outliers were then verified and corrected appropriately. A total of 20 data entry personnel were engaged during the exercise.

The captured data were exported to Statistical Package for Social Sciences (SPSS) for cleaning and analysis. The cleaned data was weighted before final analysis. The weighting of the data involved application of inflation factors derived from the selection probabilities of the EAs and households detailed in section 2.2.7, on weighting the Sample Data.

Response rate

The overall response rate stood at 85.9 per cent. Nairobi had the lowest response rate at 69.4 per cent while the highest (94.6 per cent) was realized in North Eastern. More than 95.5 per cent of all the sampled households were occupied out of which 85.9 per cent were interviewed.

This data collection examines the effects of organizational environment and funding level on the utility of criminal justice research projects sponsored by the National Institute of Justice (NIJ). The data represent a unique source of information on factors that influence the quality and utility of criminal justice research. Variables describing the research grants include NIJ office responsible for monitoring the grant (e.g., courts, police, corrections, etc.), organization type receiving the grant (academic or nonacademic), type of data (collected originally, existing, merged), and priority area (crime, victims, parole, police). The studies are also classified by: (1) sampling method employed, (2) presentation style, (3) statistical analysis employed, (4) type of research design, (5) number of observation points, and (6) unit of analysis. Additional variables provided include whether there was a copy of the study report in the National Criminal Justice Archive, whether the study contained recommendations for policy or practice, and whether the project was completed on time. The data file provides two indices--one that represents quality and one that represents utility. Each measure is generated from a combination of variables in the dataset.

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

"Those data are part of work to collect data about the status of coastal migratory flyway populations of the East Atlantic Flyway (van Roomen et al. 2013) and of partial migratory or resident waterbird populations using the same sites at this flyway. It is organized by the Wadden Sea Flyway Initiative, Wetlands International and BirdLife International in cooperation with national authorities, organisations and institutions responsible for waterbird and wetland monitoring. Besides national investments of the monitoring partners, it was funded by the Dutch Ministry of Economic Affairs through Programme Rich Wadden Sea, the organisation MAVA through BirdLife International and Wetlands International, The World Wide Fund for Nature in The Netherlands, The National Park Wadden Sea Schleswig-Holstein through the Common Wadden Sea Secretariat, The National Park Wadden Sea Niedersachsen, Vogelbescherming The Netherlands and the WeBS partnership from the UK."

Attitudes towards the EU. Protection of online personal data.

Topics: 1. Attitudes towards the EU: life satisfaction; frequency of discussions about political matters on national, European, and local level; assessment of the current situation of the national economy; expected development of the national economy in the next twelve months; most important problems in the own country, personally, and in the EU; general direction things are going in the own country and in the EU; trust in selected institutions: national government, national parliament, European Union; EU image; attitude towards the following issues: European economic and monetary union with one currency, common European defence and security policy, free trade and investment agreement between the EU and the USA, common European migration policy, common European energy policy; optimism about the future of the EU.

1. Protection of online personal data: internet use at home, at work, at school; frequency of the following online activities: use social networks, buy goods or services, use instant messaging or chat websites, use peer-to-peer software or sites to exchange movies etc., make or receive phone or video calls, banking, play games; approval of the following statements: national government asks increasingly for personal information of citizens, feeling of obligation to provide personal information online, provision of personal information as a precondition for obtaining certain products or services, respondent does not bother much with the provision of personal information, provision of personal information as an increasing part of modern life, willingness to provide personal information in return for free online services; main reasons for providing personal information online; feeling of control over personal information provided online; extent of concern about not having complete control; awareness of recent revelations about government agencies collecting personal data for the purpose of national security; impact of these revelations on personal trust regarding the use of online personal data; most serious risks of providing personal information online; attempts made to change the privacy settings of the personal profile on an online social network; assessment of the changes as easy; reasons for not changing; preferred persons or authorities to ensure the safety of online personal data; concern about the recording of everyday life activities: on the internet, in public spaces, in private spaces, via mobile phone, via payment cards, via store or loyalty cards; awareness of the conditions of collection and the further use of personal data provided online; attention payed to privacy statements on the internet; reasons for not paying attention; feeling of discomfort regarding tailored advertisements or content based on personal online activity; attitude towards the requirement of an explicit personal approval before collecting or processing personal information; trust in selected institutions with regard to protecting personal information: national public authorities, European institutions, banks and financial institutions, health and medical institutions, shops and stores, online businesses, phone companies and internet service providers; concern about personal data being used for different purposes without personal consent; importance of the possibility to transfer personal data in the case of change of online service provider; desire to be informed if personal data are stolen; preferred authorities to inform users in case personal information are stolen; importance of equal rights and protections over personal data regardless of the country in which the authority or company is located; preferred level on which the enforcement of the rules on personal data protection should be dealt with: European, national, regional or local; knowledge of a public authority in the own country responsible for protecting citizens’ rights regarding personal data; preferred body to address a complaint to regarding problems concerning the protection of personal data; data most concerned about when lost or stolen: data stored on mobile phone or tablet, data stored online in the cloud, data stored on PC.

Demography: nationality; left-right self-placement; marital status; family situation; age at end of education; sex; age; occupation; professional position; type of community; household composition and household size; possession of durable goods (entertainment electronics, Internet connection, possession of a car, a flat/a house have finished paying for or still paying for); financial difficulties during the last year; Internet use (at home, at work, at school); self-reported belonging to the working class, the middle class or the upper class of society; own voice counts in the own country and in the EU.

Additionally coded was: country; date of interview; time of the beginning of the interview; duration of the interview; number of persons present during the...

ABSTRACT: The Brazilian National School Nourishment Program (PNAE) is one of the strongest public policies of food and nutrition in the world. Besides guaranteeing free and high-quality meals to students in the basic public educational system, the PNAE also establishes that at least 30% of the resources passed on to the municipalities by the federal government should be destined for the acquisition of foodstuffs from family agriculture. However, the budget execution of the PNAE by Federal Institutes of Education has been challenging and limiting regarding the practicability of the acquisition of food from family farmers. In this study, we provided empirical evidence that constitutes an essential analytical approach still little explored in the literature, but capable of revealing operational barriers to the implementation of the program. The objective of this study was to describe the implementation and to understand the difficulties perceived by the agents responsible for the operationalization of the PNAE in the campuses of the Federal Institute of Education, Science and Technology of Bahia State (IF Baiano). Therefore, we collected secondary data from institutional documents, whereas the primary data were obtained by semi-structured interviews performed in ten campuses of IF Baiano and the rectory. We observed that in some years, the budget destined to PNAE returned without implementation, evidencing flaws in the resources management. Thus, we concluded that some challenges need to be overcome before the implementation of the Program in these Federal Institutions.

The UMETRICS 2016Q3a Dataset is comprised of two collections. The first collection includes core files in which researchers will find university financial and personnel administrative data pertaining to sponsored project expenditures at IRIS member universities during a given year. UMETRICS core files are based on administrative data drawn directly from sponsored projects, procurement, and human resources data systems on each IRIS member university’s campus. Individual campus files are de-identified, cleaned and aggregated by IRIS to produce these core files. The core files include university data on sponsored project awards, direct cost wage payments from awards to employees, purchases of goods and services from vendors, and subaward transactions to subcontractors. Additional files provide supporting information to characterize and describe IRIS member institutions, identify sub-university units responsible for particular grants, and provide additional detail on object codes included by some data providers.

In addition to core files, we are releasing crosswalk files linking UMETRICS data to external datasets at the individual and award level. In the 2016Q3a release we include match tables that: (i) link individual UMETRICS research employees to dissertation data (with a focus on dissertation topics) provided by ProQuest, and (ii) link federal awards from the National Institutes of Health (NIH), National Science Foundation (NSF) and U.S. Department of Agriculture (USDA) to detailed information about the content of grants. This documentation includes details about the data as well as the matching process. The data release includes code and original data files to allow replication and improvement of matching procedures by research users.

"Those data are part of work to collect data about the status of coastal migratory flyway populations of the East Atlantic Flyway (van Roomen et al. 2013) and of partial migratory or resident waterbird populations using the same sites at this flyway. It is organized by the Wadden Sea Flyway Initiative, Wetlands International and BirdLife International in cooperation with national authorities, organisations and institutions responsible for waterbird and wetland monitoring. Besides national investments of the monitoring partners, it was funded by the Dutch Ministry of Economic Affairs through Programme Rich Wadden Sea, the organisation MAVA through BirdLife International and Wetlands International, The World Wide Fund for Nature in The Netherlands, The National Park Wadden Sea Schleswig-Holstein through the Common Wadden Sea Secretariat, The National Park Wadden Sea Niedersachsen, Vogelbescherming The Netherlands and the WeBS partnership from the UK."

These data are based on data collected as part of the national data collection operation on the professional integration of graduates of vocational licenses.

2 new indicators were added for the 2020 session:

Employment rate: share of employed graduates among all active (employment or research) or inactive graduates

Rate of paid employment in France: share of graduates in paid employment in France among all active (employment or research) or inactive graduates

This investigation was conducted

• in December 2022, 18 and 30 months after graduation, among the graduates of the Professional License of the 2020 session;
• in December 2021, 18 and 30 months after graduation, among the graduates of the 2019 Professional License session;
• in December 2020, 18 and 30 months after graduation, among the 2018 Professional License graduates;
• in December 2019, 18 and 30 months after graduation, among the graduates of the Professional License of the 2017 session;
• in December 2018, 18 and 30 months after graduation, among the graduates of the Professional License of the 2016 session;

• in December 2017, 18 and 30 months after graduation, among the graduates of the Professional License of the 2015 session;

• in December 2016, 18 and 30 months after graduation, among the graduates of the Professional License of the 2014 session;

• in December 2015, 18 and 30 months after graduation, among the graduates of the Professional License of the 2013 session.

The insertion rate is defined as the percentage of graduates in any job out of all graduates in the labour market. It is calculated on graduates of French nationality, from initial training, who entered the labour market immediately and permanently after graduation in 2013, 2014, 2015, 2016, 2017, 2018, 2019 or 2020.

The information collected on the salary relates to the net salary, including bonuses. The wages displayed correspond to the median values on full-time jobs. On the basis of these values, an annual gross salary is estimated, on the basis of a flat rate of change from net to gross of 1.3 (average data on private sector salaries).

The survey was carried out by universities under a charter whose provisions aim to ensure comparability of results between institutions. The overall coordination and operation of the survey is the responsibility of the Ministry of Higher Education and Research.

Sources of additional data:

% of graduate scholarship holders: observed data on the population of the labour market integration survey.

Regional unemployment rate: INSEE - 4th quarter 2015 for the 2013 session, INSEE - 1st quarter 2017 for the 2014, 4th quarter 2017 for the 2015, 4th quarter 2018 for the 2016, 4th quarter 2019 for the 2017, 4th quarter 2020 for the 2018, 4th quarter 2021 for the 2019, 4th quarter 2022 for the 2020 session.

Regional median monthly net salary: INSEE DADS 2013 for the session 2013, INSEE DADS 2014 for the session 2014, INSEE DADS 2015 for the session 2015, INSEE DADS 2016 for the session 2016, INSEE DADS 2017 for the session 2017, INSEE DADS 2018 for the session 2018, INSEE DADS 2019 for the session 2019, INSEE DADS 2020 for the session 2020 for 25-29 year olds employed full-time in the socio-professional categories "Frames and higher intellectual professions" and "Intermediate professions.

Legend: nd = not available (no respondents) ns = not significant (number of respondents less than 30).

Source: 18- and 30-month job placement survey of university graduates 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020.

Collection: survey carried out by universities, treatments and synthesis carried out by MESR-SIES

Field: graduates of professional bachelor’s degrees 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020 from universities in metropolitan France and the French overseas departments (excluding Paris-Dauphine and Gustave Eiffel University (for 2020 graduates)), of French nationality, from initial training, who entered the labour market immediately and permanently after graduation.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel
- Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 3 sample consisted of 2878 households who had been interviewed at Wave 2 and a further 73 households who were interviewed at Wave 1 but were non-contact at Wave 2 were issued. A total of 2951 households (1301 in the RS and 1650 in FBiH) were issued for Wave 3. As at Wave 2, the sample could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as a Wave 2. While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at Wave 2 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design means that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefit of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are as follows:

i. Movers out of the country altogether i.e. outside FBiH and RS. This category of mover is clear. Sample members moving to another country outside FBiH and RS will be out-of-scope for that year of the survey and not eligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. The personal details of the respondent are passed between the statistical institutes and a new interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 3 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko are followed for interview. When coding entity Brcko is treated as the entity from which the household who moved into Brcko originated.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire design

Approximately 90% of the questionnaire (Annex B) is based on the Wave 2 questionnaire, carrying forward core measures that are needed to measure change over time. The questionnaire was widely circulated and changes were made as a result of comments received.

Pretesting

In order to undertake a longitudinal test the Wave 2 pretest sample was used. The Control Forms and Advance letters were generated from an Access database containing details of ten households in Sarajevo and fourteen in Banja Luka. The pretest was undertaken from March 24-April 4 and resulted in 24 households (51 individuals) successfully interviewed. One mover household was successfully traced and interviewed.
In order to test the questionnaire under the hardest circumstances a briefing was not held. A list of the main questionnaire changes was given to experienced interviewers.

Issues arising from the pretest

Interviewers were asked to complete a Debriefing and Rating form. The debriefing form captured opinions on the following three issues:

1. General reaction to being re-interviewed. In some cases there was a wariness of being asked to participate again, some individuals asking “Why Me?” Interviewers did a good job of persuading people to take part, only one household refused and another asked to be removed from the sample next year. Having the same interviewer return to the same households was considered an advantage. Most respondents asked what was the benefit to them of taking part in the survey. This aspect was reemphasised in the Advance Letter, Respondent Report and training of the Wave 3 interviewers.

2. Length of the questionnaire. The average time of interview was 30 minutes. No problems were mentioned in relation to the timing, though interviewers noted that some respondents, particularly the elderly, tended to wonder off the point and that control was needed to bring them back to the questions in the questionnaire. One interviewer noted that the economic situation of many respondents seems to have got worse from the previous year and it was necessary to listen to respondents “stories” during the interview.

3. Confidentiality. No problems were mentioned in relation to confidentiality. Though interviewers mentioned it might be worth mentioning the new Statistics Law in the Advance letter. The Rating Form asked for details of specific questions that were unclear. These are described below with a description of the changes made.

• Module 3. Q29-31 have been added to capture funds received for education, scholarships etc.

• Module 4. Pretest respondents complained that the 6 questions on "Has your health limited you..." and the 16 on "in the last 7 days have you felt depressed” etc were too many. These were reduced by half (Q38-Q48). The LSMS data was examined and those questions where variability between the answers was widest were chosen.

• Module 5. The new employment questions (Q42-Q44) worked well and have been kept in the main questionnaire.

• Module 7. There were no problems reported with adding the credit questions (Q28-Q36)

• Module 9. SIG recommended that some of Questions 1-12 were relevant only to those aged over 18 so additional skips have been added. Some respondents complained the questionnaire was boring. To try and overcome

Abstract

CAS 2022 was a comprehensive statistical undertaking for the collection and compilation of information on crop cultivation, livestock and poultry raising, aquaculture and capture fishing, agricultural economy and labour. The National Institute of Statistics (NIS) of the Ministry of Planning (MOP), and the Ministry of Agriculture, Forestry and Fisheries (MAFF), were the responsible government ministries authorized to undertake the CAS 2022. While NIS had the census and survey mandate, the MAFF was the primary user of the data produced from the survey. Technical support was also provided by the Food and Agriculture Organization of the United Nations (FAO).

The main objective of the CAS was to provide data on the agricultural situation in the Kingdom of Cambodia, to be utilized by planners and policy-makers. Specifically, the survey data are useful in:

1. Providing an updated sampling frame in the conduct of agricultural surveys;
2. Providing data at the country and regional level, with some items available at the province level;
3. Providing data on the current structure of the country's agricultural holdings, including cropping, raising livestock and poultry, and aquaculture and capture fishing activities.

The data collected and generated from this survey effort will help reflect progress towards the 2030 Sustainable Development goals for the agricultural sector, focusing on:

· Goal 1: End poverty in all forms everywhere. · Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. · Goal 5: Achieve gender equality and empower all women and girls. · Goal 6: Ensure availability and sustainable management of water and sanitation for all.

The questionnaire collected data on several aspects of the agricultural holding, including demographic information about the holder and the household members, crop production, livestock and poultry raising, aquaculture, capture fishing, and labour used by the holding. Data was collected from household agricultural holdings and juridical agricultural holdings. Only the household agricultural holdings are included in the released microdata.

Statistical Disclosure Control (SDC) methods were applied to the microdata, to protect the confidentiality of the individual data collected. Users must be aware that these methods modify the data, including suppression of some data points. This affects the aggregated values derived from the anonymized microdata, and may have other unwanted consequences, such as sampling error and bias. Additional details about SDC methods and data access are provided in the sections on 'data processing' and 'access conditions' below.

Geographic coverage

The CAS 2022 provides national coverage.

The national territory is divided in four Regions or Zones (Coastal Region, Plains Region, Plateau and Mountain Region, and Tonle Sap Region) and 25 Provinces (Banteay Meanchey, Battambang, Kampong Cham, Kampong Chhnang, Kampong Speu, Kampong Thom, Kampot, Kandal, Kep, Koh Kong, Kratie, Mondul Kiri, Otdar Meanchey, Pailin, Phnom Penh, Preah Sihanouk, Preah Vihear, Prey Veng, Pursat, Ratanak Kiri, Siem Reap, Stung Treng, Svay Rieng, Takeo, and Tboung Khmum.).

Analysis unit

Household agricultural holdings and juridical agricultural holdings. Note: The juridical agricultural holdings are not included in the released microdata.

Universe

Agricultural households, i.e. holdings in the household sector that are involved in agricultural activities, including the growing of crops, raising of livestock or poultry, and aquaculture or capture fishing activities. It was not considered a minimum threshold to determine a household's engagement in the above-mentioned activities.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling approach for the CAS 2022 relied fully upon the sampling of CAS 2021 utilising a panel approach. The CAS 2021 had used statistical methods to select a representative sample of enumeration areas throughout Cambodia from the 2019 General Population Census of Cambodia Sampling Frame. Households within these EAs were screened for any agricultural activity. Using this basic information, the agricultural households were stratified and sampled for additional data collection. Juridical holdings, which are farm enterprises operated by corporations or government institutions, were also surveyed based on listings provided by MAFF and other governmental offices with knowledge of agricultural juridical holdings.

For the CAS 2021, and therefore CAS 2022 using its panel approach, the 2019 General Population Census Sampling Frame was utilized. This frame consisted of around 14,500 villages and 38,000 Enumeration Areas (EAs). For each village, the following information was available: province, district, commune, type (rural/urban), number of EAs and number of households. The target population comprised the households that were engaged in agriculture, fishery and/or aquaculture. Given their low number of rural villages, the following districts were excluded from the frame:

• Province Preah Sihanouk, District Krong Preah Sihanouk
• Province Siemreap, District Krong Siem Reab
• Province Phnom Penh, District Chamkar Mon
• Province Phnom Penh, District Doun Penh
• Province Phnom Penh, District Prampir Meakkakra
• Province Phnom Penh, District Tuol Kouk
• Province Phnom Penh, District Ruessei Kaev
• Province Phnom Penh, District Chhbar Ampov

Since the number of rural households per EA was not known from the 2019 census, to calculate the number of rural households in each province, the sum of the households in the villages that were classified as rural was computed. The listing operation in each sampled EA was conducted for the CAS 2021 to identify the target population, i.e., the households engaged in agricultural activities.

For this survey, there was no minimum threshold set to determine a household's engagement in agricultural activities. This differs from the procedures used during the 2013 Agriculture Census (and that would be used in the 2023 Agriculture Census later), in which households were eligible for the survey if they grew crops on at least 0.03 hectares and/or had a minimum of 2 large livestock and/or 3 small livestock and/or 25 poultry. The procedure used in the CAS, which had no minimum land area or livestock or poultry inventory, allowed for smaller household agricultural holdings to have the potential to be selected for the survey. However, based on the sampling procedure indicated below, household agricultural holdings with larger land areas or more livestock or poultry were identified and associated with different sampling strata to ensure the selection of some of them.

The CAS 2021 and therefore CAS 2022 used a two-stage stratified sampling procedure, with EAs as primary units and households engaged in agriculture as secondary units. In the CAS 2021 and CAS 2022, 1,381 EAs and 12 agricultural households for each EA were selected, for a total planned sample size of 16,572 households. The 1,381 EAs were allocated to the provinces (statistical domains) proportionally to the number of rural households. To select the EAs within each province, the villages were ordered by district, commune, and then by type of village (Rural-Urban). Systematic sampling was then performed, with probability proportional to size (number of households). After attrition from the previous year, the total effective sample size of the survey was 15,751 agricultural households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Once the enumerators collected the survey data for an agricultural household, they submitted the completed questionnaire via Survey Solutions to their data supervisors who, in turn, carried out quality checks. If there errors or suspicious data were detected, the data supervisor would return the record to the enumerator to address the issues with the respondent if needed, and the corrected record would be re-submitted to the data supervisor. Once the records were validated by the data supervisors, they would approve them for final review by headquarters staff.

At the survey headquarters, the completed questionnaires were received after being approved by the data supervisors. If any issues or suspicious data were discovered during the headquarters review, the records could be returned to the enumerator for verification or correction if needed. Documentation on how to review questionnaire data for suspicious items or outliers was provided to both data supervisors and headquarters staff.

The data review and calculation of the survey estimates was undertaken using the RStudio software tool. Validation of the data began even when the questionnaires were being designed in the CAPI tool, as Survey Solutions allows for consistency checks to be built into the data collection tool. As soon as completed records were returned during the data collection stage, additional consistency checks were completed, evaluating the ranges for certain items, and verifying any outlier records with the enumerator and/or respondent. Moreover, when the data was cleaned, another step was conducted to impute the missing values derived from item non-response.

STATISTICAL DISCLOSURE CONTROL (SDC):

Microdata are disseminated as Public Use Files under the terms and conditions indicated at the NIS Microdata Catalog (https://microdata.nis.gov.kh/), as indicated in the section 'access conditions'.

In addition, anonymization methods have been applied to the microdata files before their dissemination, to protect the confidentiality of the statistical units (e.g. individuals) from which the data were