TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

The dataset that we provide is composed of a csv file containing the answers of responders to our questionnaire conducted to explore perceptions and feelings on the COVID-19 pandemic. The survey was conducted from June 27 to July 2 2022 among university students and adult residents of Milan, Italy, and New York City, NY, U.S.A.. The two target demographics for this study were adult residents of the two cities who were employed at the beginning of 2020 and students who attended university during 2020 or joined during the pandemic. The survey was accompanied by a promotional video and an introductory paragraph describing the objective of the study, and it was shared through social media platforms, on specialized social media groups, and on university students’ mailing lists. The total number of questions asked is a maximum of 20, variable depending on answers given by a user since we employed branching based on previous answers. This feature was particularly useful in creating questions that were specific to a subset of the sample population The topics of questions cover the following broad areas: Relationships: Multiple Choice and sorting/ranking questions designed to understand who the respondents spent lockdown with, if they managed to keep in touch with those they could not meet, and to family, friends and intimate relationships during the pandemic Policies: Likert scale questions measuring agreement with measures put in place in both Milan and New York Personal Life: questions about one’s priorities before and during the pandemic Occupation: Multiple Choice questions about one’s occupation during the pandemic and feelings towards work or university Post-pandemic: Likert scale questions about one's perception of contagion threats and feelings of normalcy at the time they responded to the survey Demographics: Multiple choice questions to describe the pool of respondents and control sample bias The types of the questions are of one of the following types: Multiple choice (one or more selections or single selection) Ranking Numeric scale (1-5 or 1-10) The “ranking” question type allowed users to sort a list of items in descending order of importance. In the dataset the column name represents the ranking given to the item, e.g. 1. highest priority.

Explore gender statistics related to families and households, including data on both sexes, percent of total for both sexes, total live births, population, and residential information. Access valuable insights and trends for countries such as Portugal, Belgium, Spain, France, Italy, United Kingdom, United States, and many more.

Both sexes, Percent of Total for Both Sexes, Total Live Births, Population, Residential, birth

Portugal, Belgium, Spain, Bosnia and Herzegovina, France, Denmark, Italy, Uzbekistan, Bulgaria, United Kingdom, Slovenia, Czechia, Poland, Ukraine, Latvia, Sweden, Iceland, Armenia, Georgia, Canada, Montenegro, Hungary, United States, Andorra, Republic of Moldova, Croatia, Malta, San Marino, Turkmenistan, Azerbaijan, Kyrgyzstan, North Macedonia, Russian Federation, Greece, Luxembourg, Monaco, Slovakia, Norway, Tajikistan, Albania, Liechtenstein, Serbia, Switzerland, Lithuania, Estonia, Turkiye, Cyprus, Germany, Finland, Ireland, Israel, Kazakhstan, Austria, Belarus, Netherlands, RomaniaFollow data.kapsarc.org for timely data to advance energy economics research.Source: UNECE Statistical Database, compiled from national and international (Eurostat, UN Statistics Division Demographic Yearbook, WHO European health for all database and UNICEF TransMONEE) official sources.Definition: A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached.General note: Data come from registers, unless otherwise specified. In years 2003 and before, the number of live births for girl child and boy child may not add up to the number for both sexes (Total) due to the rounding up of numbers.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

Abstract

This study provides an update on measures of educational attainment for a broad cross section of countries. In our previous work (Barro and Lee, 1993), we constructed estimates of educational attainment by sex for persons aged 25 and over. The values applied to 129 countries over a five-year intervals from 1960 to 1985.

The present study adds census information for 1985 and 1990 and updates the estimates of educational attainment to 1990. We also have been able to add a few countries, notably China, which were previously omitted because of missing data.

Dataset:

Educational attainment at various levels for the male and female population. The data set includes estimates of educational attainment for the population by age - over age 15 and over age 25 - for 126 countries in the world. (see Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality, AER, Papers and Proceedings, 86(2), pp. 218-223 and also see "International Data on Education", manuscipt.) Data are presented quinquennially for the years 1960-1990;

Educational quality across countries. Table 1 presents data on measures of schooling inputs at five-year intervals from 1960 to 1990. Table 2 contains the data on average test scores for the students of the different age groups for the various subjects.Please see Jong-Wha Lee and Robert J. Barro, "Schooling Quality in a Cross-Section of Countries," (NBER Working Paper No.w6198, September 1997) for more detailed explanation and sources of data.

Geographic coverage

The data set cobvers the following countries: - Afghanistan - Albania - Algeria - Angola - Argentina - Australia - Austria - Bahamas, The - Bahrain - Bangladesh - Barbados - Belgium - Benin - Bolivia - Botswana - Brazil - Bulgaria - Burkina Faso - Burundi - Cameroon - Canada - Cape verde - Central African Rep. - Chad - Chile - China - Colombia - Comoros - Congo - Costa Rica - Cote d'Ivoire - Cuba - Cyprus - Czechoslovakia - Denmark - Dominica - Dominican Rep. - Ecuador - Egypt - El Salvador - Ethiopia - Fiji - Finland - France - Gabon - Gambia - Germany, East - Germany, West - Ghana - Greece - Grenada - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong - Hungary - Iceland - India - Indonesia - Iran, I.R. of - Iraq - Ireland - Israel - Italy - Jamaica - Japan - Jordan - Kenya - Korea - Kuwait - Lesotho - Liberia - Luxembourg - Madagascar - Malawi - Malaysia - Mali - Malta - Mauritania - Mauritius - Mexico - Morocco - Mozambique - Myanmar (Burma) - Nepal - Netherlands - New Zealand - Nicaragua - Niger - Nigeria - Norway - Oman - Pakistan - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Romania - Rwanda - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Solomon Islands - Somalia - South africa - Spain - Sri Lanka - St.Lucia - St.Vincent & Grens. - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syria - Taiwan - Tanzania - Thailand - Togo - Tonga - Trinidad & Tobago - Tunisia - Turkey - U.S.S.R. - Uganda - United Arab Emirates - United Kingdom - United States - Uruguay - Vanuatu - Venezuela - Western Samoa - Yemen, N.Arab - Yugoslavia - Zaire - Zambia - Zimbabwe

With the proliferation of tobacco products, there might be a need for more complex models than current two-product models. We have developed a three-product model able to represent interactions between three products in the marketplace. We also investigate if using several implementations of two-product models could provide sufficient information to assess 3 coexisting products. Italy is used as case-study with THPs and e-cigarettes as the products under investigation. We use transitions rates estimated for THPs in Japan and e-cigarettes in the USA to project what could happen if the Italian population were to behave as the Japanese for THP or USA for e-cigarettes. Results suggest that three-product models may be hindered by data availability while two product models could miss potential synergies between products. Both, THP and E-Cigarette scenarios, led to reduction in life-years lost although the Japanese THP scenario reductions were 3 times larger than the USA e-cigarette projections.

Beurteilung von Sicherheitsfragen. Ost-West-Vergleich. Themen: Zufriedenheit mit dem Lebensstandard; Einstellung zu Frankreich, Großbritannien, Italien, USA, UdSSR, Rotchina, Westdeutschland; präferierte Ost-West-Orientierung des eigenen Landes und Übereinstimmung der Landesinteressen mit den Interessen ausgewählter Länder; Beurteilung der Friedensbemühungen Amerikas, der Sowjetunion und Rotchinas; Beurteilung der Außenpolitik der USA und der UdSSR; Vertrauen in die außenpolitischen Fähigkeiten der USA; mächtigstes Land der Erde, derzeit und zukünftig; Vergleich der USA mit der UdSSR bezüglich der militärischen und wirtschaftlichen Stärke, der Atomwaffen und auf den Gebieten Kultur, Wissenschaft, Weltraumforschung, Bildung sowie der wirtschaftlichen Aussichten für den Durchschnittsbürger; Bedeutung einer Mondlandung; Sowjetbürger oder Amerikaner als erster auf dem Mond; vermutete Bedeutung der Weltraumforschung für die militärische Entwicklung; Einstellung zu einem vereinten Europa und zu einem Beitritt Großbritanniens zum Gemeinsamen Markt; präferierte Beziehung eines vereinten Europas zu den Vereinigten Staaten; gerechter Anteil an den angenehmen Dingen des Lebens; fehlende Anstrengung oder Schicksal als Gründe für Armut; allgemeine Lebenszufriedenheit; perzipierte Zuwachsrate der Bevölkerung im Lande und Präferenz für Bevölkerungszuwachs; Einstellung zu einem Anwachsen der Weltbevölkerung; präferierte Maßnahmen zur Bekämpfung einer Überbevölkerung; Einstellung zu einem Geburtenkontrollprogramm in den Entwicklungsländern und im eigenen Lande; gegenwärtige Politikeridole in Europa und in der übrigen Welt; Einstellung zur Abrüstung; Vertrauen in die Bündnispartner; Bekanntheitsgrad der Nato und Einschätzung ihrer derzeitigen Stärke; Einstellung zu einer europäischen Atomstreitmacht; gewünschte und eingeschätzte Loyalität der Amerikaner gegenüber den Nato-Bündnispartnern; Einschätzung der Entwicklung der UNO; gleiches Mitspracherecht für alle UNO-Mitglieder; gewünschte Verteilung der UNO-Finanzlasten; Einstellung zu einer Aufnahme Rotchinas in die Vereinten Nationen; Kenntnisse über Kämpfe in Vietnam; Einstellung zum Vietnamkrieg; Einstellung zum Verhalten Amerikas, Rotchinas und der Sowjetunion in diesem Konflikt; Einstellung zum Rückzug amerikanischer Truppen aus Vietnam und präferierte Haltung des eigenen Landes in diesem Konflikt und im Falle eines Konfliktes mit Rotchina; Beurteilung der Behandlung von Farbigen in Großbritannien, Amerika und der Sowjetunion; Beurteilung der amerikanischen Bundesregierung und der amerikanischen Bevölkerung in bezug auf die Gleichberechtigung für Neger; Bekanntheitsgrad der chinesischen Atombombenversuche; Auswirkungen dieses Versuchs auf die militärische Stärke Rotchinas; Einstellung zu amerikanischen Privatinvestitionen in der Bundesrepublik; einflußreichste Gruppen und Organisationen im Lande; Parteipräferenz; Religiosität. Interviewerrating: Schichtzugehörigkeit des Befragten. Zusätzlich verkodet wurde: Anzahl der Kontaktversuche; Interviewdatum. Opinion on questions concerning security policy. East-West comparison. Topics: Satisfaction with the standard of living; attitude to France, Great Britain, Italy, USA, USSR, Red China and West Germany; preferred East-West-orientation of one´s own country and correspondence of national interests with the interests of selected countries; judgement on the American, Soviet and Red Chinese peace efforts; judgement on the foreign policy of the USA and the USSR; trust in the foreign policy capabilities of the USA; the most powerful country in the world, currently and in the future; comparison of the USA with the USSR concerning economic and military strength, nuclear weapons and the areas of culture, science, space research, education as well as the economic prospects for the average citizen; significance of a landing on the moon; Soviet citizen or American as first on the moon; assumed significance of space research for military development; attitude to a united Europe and Great Britain´s joining the Common Market; preferred relation of a united Europe to the United States; fair share of the pleasant things of life; lack of effort or fate as reasons for poverty; general contentment with life; perceived growth rate of the country´s population and preference for population growth; attitude to the growth of the population of the world; preferred measures against over-population; attitude to a birth control program in the developing countries and in one´s own country; present politician idols in Europe and in the rest of the world; attitude to disarmament; trust in the alliance partners; degree of familiarity with the NATO and assessment of its present strength; attitude to a European nuclear force; desired and estimated loyalty of the Americans to the NATO alliance partners; evaluation of the development of the UN; equal voice for all members of the UN; desired distribution of the UN financial burdens; attitude to an acceptance of Red China in the United Nations; knowledge about battles in Vietnam; attitude to the Vietnam war; attitude to the behavior of America, Red China and the Soviet Union in this conflict; attitude to the withdrawal of American troops from Vietnam and preferred attitude of one´s own country in this conflict and in case of a conflict with Red China; opinion on the treatment of colored people in Great Britain, America and the Soviet Union; judgement on the American Federal Government and on the American population regarding the equality of Negros; degree of familiarity with the Chinese nuclear tests; effects of this test on the military strength of Red China; attitude to American private investments in the Federal Republic; the most influential groups and organizations in the country; party preference; religiousness. Interviewer rating: social class of respondent. Additionally encoded were: number of contact attempts; date of interview.