Congo, The Democratic Republic of the CD: Death Rate: Crude: per 1000 People data was reported at 9.934 Ratio in 2016. This records a decrease from the previous number of 10.187 Ratio for 2015. Congo, The Democratic Republic of the CD: Death Rate: Crude: per 1000 People data is updated yearly, averaging 17.029 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 22.794 Ratio in 1960 and a record low of 9.934 Ratio in 2016. Congo, The Democratic Republic of the CD: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data was reported at 42.280 Ratio in 2016. This records a decrease from the previous number of 42.809 Ratio for 2015. Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 46.094 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 46.903 Ratio in 1965 and a record low of 42.280 Ratio in 2016. Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

The overall ACE project is motivated by the need to better understand the behaviour of non-state armed groups in perpetrating atrocity crimes such as crimes against humanity, ethnic cleansing and war crimes. The data collection is based on six countries Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria, and Somalia with a focus on non-state actor perpetrated atrocity events. The aim is to have a fine-grained event data collection of different types of atrocity crimes in these countries. These event types are derived from the Rome Statute. More specifically, the unit of observation in ACE is the event where a named state or non-state actor is involved on a given day in a specific location. Each individual event is covered with the best precision regarding the type of event, location, perpetrator and victims.Since 2010, there has been a 'dramatic resurgence' of violent conflict around the world (United Nations, 2018, p. v). As part of this trend, mass atrocity crimes, defined as genocide, war crimes, crimes against humanity, and ethnic cleansing (GWCE), have become 'the new normal' (Human Rights Watch 2018). At this time of writing, the Global Centre for the Responsibility to Protect (GCR2P) identifies seven countries that are 'currently' experiencing GWCE, three at 'imminent risk', seven of 'serious concern', and thirteen being 'monitored' because they have experienced GWCE in the recent past (Global Centre for the Responsibility to Protect 2019). These crises have seen millions of people killed, tens of thousands raped, and underpin an unprecedented refugee crisis. Although mass violence is not a new phenomenon, non-state armed groups such as Al Qaeda, Islamic State, Boko Haram, Lord's Resistance Army, and Al-Shabaab are increasingly playing a critical role in the perpetration of atrocity crimes leading to key policymakers calling for urgent research on this specific threat (see case for support). Responding to this new reality, the project answers the following primary research question: under what conditions do non-state armed groups perpetrate GWCE? The funding will enable us to develop the first dataset in the world that collects systematic evidence on non-state armed groups perpetrating GWCE, which we call 'Atrocity Crime Events' (ACE) dataset. To do this, we will analyse six countries and three themes. The former refers to the Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria and Somalia. The latter focuses on i) interactions, for example, between the non-state armed group[s] themselves, other actors such as the government, and external actors such as UN peacekeepers, ii) local factors, for instance, geography, economics, population density, as well as natural resources, and iii) group characteristics, such as age, ideology, and external support. The scientific impact develops in three stages. First, the unique dataset 'ACE' will provide the necessary information to run statistical analysis to explain why, when, and where mass atrocities happen in our six chosen countries. Second, we will develop hypothesis based on our three themes that can be tested through future academic inquiry. Third, the project seeks to drive forward quantitative research into the causes of non-state armed groups perpetrating mass violence. This advance in knowledge will allow us to provide policy recommendations in order to improve international, regional, and national strategies toward mass atrocity prevention with a specific focus on policymakers in the United Nations (UN), the European Union (EU), the United Kingdom (UK), and Africa (the four case study governments and organisations such as the African Union). We will work with three project partners, GCR2P (New York and Geneva), Aegis Trust (Kigali), and Protection Approaches (London), as well as an advisory board consisting of Alex Bellamy, Gyorgy Tatar, Ivan Simonovic, Karen E. Smith, and Kristian Skrede Gleditsch. As part of our impact strategy, we will hold end of project workshops in London, New York, and Kigali. Outputs will include i) publicly available dataset and codebook, ii) six articles in high ranking journals, iii) an Analysis Framework for the United Nations Office on Genocide Prevention and the RtoP, iv) co-created policy reports with each project partner focusing on the UN, the UK, the EU, and African mass atrocity prevention strategies, v) blogposts, vi) infographics, and vii) presentations at conferences and policy-orientated meetings. The data collection methodology is based on coding news reports extracted from LexisNexis. The extraction of news reports from LexisNexis has been narrowed down by using specific search terms for each event type, including the countries in this project. The focus is primarily on English language sources and where necessary, the geography filter is used to narrow down results based on the location of the event. Once a set of news reports have been identified from Lexis Nexis, the coders skim through the reports based on headlines/short descriptions and select to read through the ones that seem to constitute an event (as opposed to, for example, reports about UN meetings to discuss atrocities etc.). The coders then write a short description of the event on the dataset and code the rest of the variables in the dataset with best precision possible. The coding of the events is based on ACE codebook and is conducted by human coders, each specialising in one of the countries in question.

This raster dataset contains land cover classification in Brazzaville in 2005 and 2010 derived from SPOT5 imagery.

Land cover classes in the attribute table are as follows:

Class 1 - Regular Residential (small planned buildings)
Class 2- Regular Residential (small unplanned buildings)
Class 3 - Commercial/Industrial (large buildings)
Class 4 - Natural (Vegetation/Soil/non built-up

This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost.

When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394

The project examines households’ daily management, financial governance, access to water and other basic social services in the city of Goma, in the eastern Democratic Republic of the Congo (DRC). It uses an innovative mix of social network research, ethnography and governance diaries to gain in-depth data to reveal how residents navigate public authority in an insecure environment and cope with unforeseen shocks. The data is collected every two weeks by five Congolese researchers over a period of eleven months. Project leads will analyse the data using complementary qualitative and quantitative approaches, yielding a range of outputs from co-authored academic articles to policy briefs and blogs written by researchers. A paper examining the political economy of water services in Goma will also be researched and written in mid-2019. This anonymised data concerns the household financial diaries of 23 households in three neighbourhoods of Goma, the provincial capital of North Kivu, in the Democratic Republic of the Congo (DRC) between February 2019 and March 2020. Our research centred on man- and woman-headed households of low and middle socioeconomic strata. It is designed to be used alongside ethnographic interview and social network data which was also collected but is not in this repository due to sensitivities.CPAID will address critical questions that have bedevilled the outside world's engagement with governance of fragile, conflict affected, marginal and impoverished populations. In these places inclusive growth has proved elusive. We propose a different starting point. Rather than anticipating transitions to accountable and capable Western government familiar to policymakers, CPAID prioritises the everyday lived realities of ordinary people in conflict-affected and fragile situations. In these places, the foundations of such growth are far more widespread and pervasive than state institutions. Through the lens of public authority, CPAID researchers seek to understand how governance actually functions in such circumstances, what forms of growth does this accomplish, and can actually existing forms of inclusive growth be promoted by development practitioners. Only a historically-informed, contextual and interdisciplinary analysis of how political, economic and social factors interact can achieve a full understanding of 'real governance' in conflict affected in places. Understanding these dynamics is critical to inform new and improved models of international development which will actually provide or enhance firm foundations for future inclusive growth. CPAID will explore how forms of public authority shape and are shaped by a set of interlocking global challenges that pose both risks and opportunities for international development and inclusive growth: namely, the provision of security and justice; migration, displacement and situations of endemic violence; global health threats; control and allocation of resources; and advances in media and information technologies. CPAID will fill a serious evidence gap about on the ground realities in large areas of Africa which currently affect other regions, including Europe. The CPAID team includes world- leading authorities on, Uganda, Kenya, Sierra Leone, Rwanda, Burundi, Democratic Republic of Congo, South Sudan, Sudan, Somalia, Ethiopia, and Central African Republic. Our primary focus is on public authority as perceived, understood and experienced by populations in locations of research. Research over the last decade or so has challenged prevailing assumptions embodied in the 'failed states' discourse, namely that in the absence of western-style governance institutions, fragile and conflict affected societies collapse or flounder. CPAID will undertake research which can help us understand the various ways in which actual forms of public authority work. This approach is desperately needed in development policy. Conventional conflict and post-conflict state building processes, premised on Weberian notions of the state, are hugely expensive and too often unsuccessful or arguably even counter-productive. Moreover, with the rise of 'resilience thinking', donors are increasingly acknowledging that the world is a place of 'radical uncertainty', and determined, in the words of DFID to 'embrace uncertainty as an opportunity to... bounce back better'. This has underpinned a new, but under-researched agenda to find more cost-effective and culturally 'embedded' forms of governance that donors can support. This research will also take place in the context of massive investments to provide internet connectivity. The next five years will witness unprecedented efforts to connect millions of people who do not currently have internet access in Africa, in remote and borderland areas. Examining the role of new technologies, including social media, in reshaping public authorities and governments will provide crucial entry points to develop policies to achieve new forms of inclusive growth.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Correlates of monthly disruption magnitude in service volume.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes North and South Kivu, Ituri, and Haut-Uele because of security risks. The excluded area represents approximately 20% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Congo, Dem. Rep. was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Time series data for the statistic Adjusted savings: particulate emission damage (% of GNI) and country Congo, Dem. Rep.. Indicator Definition:Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates.The indicator "Adjusted savings: particulate emission damage (% of GNI)" stands at 1.03 as of 12/31/2021, the lowest value at least since 12/31/1995, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -4.58 percent compared to the value the year prior.The 1 year change in percent is -4.58.The 3 year change in percent is -13.31.The 5 year change in percent is -19.62.The 10 year change in percent is -40.53.The Serie's long term average value is 2.07. It's latest available value, on 12/31/2021, is 50.38 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2021, to it's latest available value, on 12/31/2021, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1994, to it's latest available value, on 12/31/2021, is -72.95%.

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Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal 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
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.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]