The current dataset is a subset of a large data collection based on a purpose-built survey conducted in seven middle-income countries in the Global South: Chile, Colombia, India, Kenya, Nigeria, Tanzania, South Africa and Vietnam. The purpose of the collected variables in the present dataset aims to understanding public preferences as a critical way to any effort to reduce greenhouse gas emissions. There are many studies of public preferences regarding climate change in the Global North. However, survey work in low and middle-income countries is limited. Survey work facilitating cross-country comparisons not using the major omnibus surveys is relatively rare.
We designed the Environment for Development (EfD) Seven-country Global South Climate Survey (the EfD Survey) which collected information on respondents’ knowledge about climate change, the information sources that respondents rely on, and opinions on climate policy. The EfD survey contains a battery of well-known climate knowledge questions and questions concerning the attention to and degree of trust in various sources for climate information. Respondents faced several ranking tasks using a best-worst elicitation format. This approach offers greater robustness to cultural differences in how questions are answered than the Likert-scale questions commonly asked in omnibus surveys. We examine: (a) priorities for spending in thirteen policy areas including climate and COVID-19, (b) how respiratory diseases due to air pollution rank relative to six other health problems, (c) agreement with ten statements characterizing various aspects of climate policies, and (d) prioritization of uses for carbon tax revenue. The company YouGov collected data for the EfD Survey in 2023 from 8400 respondents, 1200 in each country. It supplements an earlier survey wave (administered a year earlier) that focused on COVID-19. Respondents were drawn from YouGov’s online panels. During the COVID-19 pandemic almost all surveys were conducted online. This has advantages and disadvantages. Online survey administration reduces costs and data collection times and allows for experimental designs assigning different survey stimuli. With substantial incentive payments, high response rates within the sampling frame are achievable and such incentivized respondents are hopefully motivated to carefully answer the questions posed. The main disadvantage is that the sampling frame is comprised of the internet-enabled portion of the population in each country (e.g., with computers, mobile phones, and tablets). This sample systematically underrepresents those with lower incomes and living in rural areas. This large segment of the population is, however, of considerable interest in its own right due to its exposure to online media and outsized influence on public opinion. The data includes respondents’ preferences for climate change mitigation policies and competing policy issues like health. The data also includes questions such as how respondents think revenues from carbon taxes should be used. The outcome provide important information for policymakers to understand, evaluate, and shape national climate policies. It is worth noting that the data from Tanzania is only present in Wave 1 and that the data from Chile is only present in Wave 2.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

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The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.This year, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.The 2015 GHI has been calculated for 117 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.This year's GHI reflects the most recent available country-level data and projections available between 2010 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2015. The 1990, 1995, 2000, 2005, and 2015 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.The four component indicators used to calculate the GHI scores draw upon data from the following sources:1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2015 GHI scores. Undernourishment data and projections for the 2015 GHI are for 2014-2016.2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; statistical tables from UNICEF; and the latest national survey data for India from UNICEF India. For the 2015 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2010-2014.3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2015 GHI scores. For the 2015 GHI, data on child mortality are for 2013.