• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

Explore World Bank Health, Nutrition and Population Statistics dataset featuring a wide range of indicators such as School enrollment, UHC service coverage index, Fertility rate, and more from countries like Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

School enrollment, tertiary, UHC service coverage index, Wanted fertility rate, People with basic handwashing facilities, urban population, Rural population, AIDS estimated deaths, Domestic private health expenditure, Fertility rate, Domestic general government health expenditure, Age dependency ratio, Postnatal care coverage, People using safely managed drinking water services, Unemployment, Lifetime risk of maternal death, External health expenditure, Population growth, Completeness of birth registration, Urban poverty headcount ratio, Prevalence of undernourishment, People using at least basic sanitation services, Prevalence of current tobacco use, Urban poverty headcount ratio, Tuberculosis treatment success rate, Low-birthweight babies, Female headed households, Completeness of birth registration, Urban population growth, Antiretroviral therapy coverage, Labor force, and more.

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

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Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Rising global food insecurity driven by population growth needs urgent measure for universal access to food. This research employs Comparative Performance Analysis (CPA) to evaluate the Global Food Security Index (GFSI), its components [Affordability (AF), Availability (AV), Quality & Safety (Q&S) and Sustainability & Adaptation (S&A)] in tandem with Annual Population Change (APC) for world’s five most populous countries (India, China, USA, Indonesia and Pakistan) using dataset spanning from 2012 to 2022. CPA is applied using descriptive analysis, correlation analysis, Rule of Thumb (RoT) and testing of hypothesis etc. RoT is used with a new analytical approach by applying the significance measures for correlation coefficients. The study suggests that India should enhance its GFSI rank by addressing AF and mitigating the adverse effects of APC on GFSI with a particular focus on Q&S and S&A. China needs to reduce the impact of APC on GFSI by prioritizing AV and S&A. The USA is managing its GFSI well, but focused efforts are still required to reduce APC’s impact on Q&S and S&A. Indonesia should improve across all sectors with a particular focus on APC reduction and mitigating its adverse effects on AF, AV, and S&A. Pakistan should intensify efforts to boost its rank and enhance all sectors with reducing APC. There is statistically significant and negative relation between GFSI and APC for China, Indonesia and found insignificant for others countries. This study holds promise for providing crucial policy recommendations to enhance food security by tackling its underlying factors.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

Explore Saudi Arabia's education development indicators , including net attendance rates, literacy rates, teacher salaries, and more. Discover valuable insights and trends in education data for Saudi Arabia and other countries in the region.

UIS, attendance rate, literacy rate, teacher salaries, education indicators, net enrolment rate, drop-out rate, population, schooling

Saudi Arabia, Kuwait, Oman, Qatar, Bahrain, China, India

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Note: © 2016 The World Bank Group, All Rights Reserved.Saudi Arabia education indicator related dataset from the world bank. There are over 1300 series in the dataset, we have selected those relevant to education category. Checkout other related dataset Population, Health and Employment in demographic category of our portal.Citation: "World Development Indicators| World Databank". Databank.worldbank.org. N.p., 2016. Web. 10 Mar. 2016.

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

The aim of the article is to compare health system outcomes in the BRICS countries, assess the trends of their changes in 2000−2017, and verify whether they are in any way correlated with the economic context. The indicators considered were: nominal and per capita current health expenditure, government health expenditure, gross domestic product (GDP) per capita, GDP growth, unemployment, inflation, and composition of GDP. The study covered five countries of the BRICS group over a period of 18 years. We decided to characterize countries covered with a dataset of selected indicators describing population health status, namely: life expectancy at birth, level of immunization, infant mortality rate, maternal mortality ratio, and tuberculosis case detection rate. We constructed a unified synthetic measure depicting the performance of individual health systems in terms of their outcomes with a single numerical value. Descriptive statistical analysis of quantitative traits consisted of the arithmetic mean (xsr), standard deviation (SD), and, where needed, the median. The normality of the distribution of variables was tested with the Shapiro–Wilk test. Spearman's rho and Kendall tau rank coefficients were used for correlation analysis between measures. The correlation analyses have been supplemented with factor analysis. We found that the best results in terms of health care system performance were recorded in Russia, China, and Brazil. India and South Africa are noticeably worse. However, the entire group performs visibly worse than the developed countries. The health system outcomes appeared to correlate on a statistically significant scale with health expenditures per capita, governments involvement in health expenditures, GDP per capita, and industry share in GDP; however, these correlations are relatively weak, with the highest strength in the case of government's involvement in health expenditures and GDP per capita. Due to weak correlation with economic background, other factors may play a role in determining health system outcomes in BRICS countries. More research should be recommended to find them and determine to what extent and how exactly they affect health system outcomes.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

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Context

This data set come from UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/census+income

Data Set Information:

Prediction task is to determine whether a person makes over 50K a year from the analysis of 13 predictors.

Content

age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

Acknowledgements

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

Description of fnlwgt (final weight)

The weights on the CPS files are controlled to independent estimates of the civilian non-institutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.

These are:

1. A single cell estimate of the population 16+ for each state.
2. Controls for Hispanic Origin by age and sex.
3. Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.

The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population.

People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.

Summary

Data Set Characteristics: Multivariate Area: Social Attribute Characteristics: Categorical, Integer Number of Attributes: 14 Date Donated: 1996-05-01 Associated Tasks: Classification Missing Values? Yes

This dataset contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The indicator bring together the OECD's statistics, indicators and measures of progress. The dataset covers OECD countries as well as BRIICS economies (Brazil, Russian Federation, India, Indonesia, China and South Africa), and selected countries when possible. The indicators are selected according to well specified criteria and embedded in a conceptual framework, which is structured around four groups to capture the main features of green growth: Environmental and resource productivity, to indicate whether economic growth is becoming greener with more efficient use of natural capital and to capture aspects of production which are rarely quantified in economic models and accounting frameworks; The natural asset base, to indicate the risks to growth from a declining natural asset base; Environmental quality of life, to indicate how environmental conditions affect the quality of life and wellbeing of people; Economic opportunities and policy responses, to indicate the effectiveness ofpolicies in delivering green growth and describe the societal responses needed to secure business and employment opportunities.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

Explore the dataset on midyear population statistics for 2015, including data on non-infectious diseases, infectious diseases, accidents, malnutrition, congenital diseases, and more. Gain insights on population health trends globally.

Non-infectious, Midyear population, Annual, Infectious disease, Accident/Trauma, Malnutrition, Congenital disease, Other (including ageing), Disease, Health, Population

China, Germany, India, Japan, Russia, United States Follow data.kapsarc.org for timely data to advance energy economics research.

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode. Face-to-face interview: CAPI (Computer Assisted Personal Interview). Face-to-face interview: PAPI (Paper and Pencil Interview). Telephone interview: CATI (Computer Assisted Telephone Interview). Self-administered questionnaire: CAWI (Computer-Assisted Web Interview). Self-administered questionnaire: Paper. In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2022 is face to face (interviewer-administered). Several countries employed self-administered interview or mixed-mode approach to data collection: Australia (CAWI & postal survey); Canada (CAWI); Hong Kong SAR (PAPI & CAWI); Malaysia (CAWI & PAPI); Netherlands (CAWI); USA (CAWI & CATI). The WVS Master Questionnaire was provided in English, Arabic, Russian and Spanish. Each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. WVSA Secretariat and Data archive monitored the translation process; every translation is subject to multi-stage validation procedure before the fieldwork can be started. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country/ territory] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). Research area: Andorra (AD); Argentina (AR); Armenia (AM); Australia (AU); Bangladesh (BD); Bolivia (BO); Brazil (BR); Canada (CA); Colombia (CO); Chile (CL); China (CN); Cyprus (CY); Ecuador (EC); Egypt (EG); Ethiopia (ET); Germany (DE); Greece (GR); Guatemala (GT); Hong Kong SAR PRC (HK); Indonesia (ID); Iran (IR); Iraq (IQ); Japan (JP); Jordan (JO); Kazakhstan (KZ); Kenya (KE); Kyrgyzstan (KG); Lebanon (LB); Libya (LY); Macao SAR PRC (MO); Malaysia (MY); Maldives (MV); Mexico (MX); Mongolia (MN); Morocco (MA); Myanmar (MM); Netherlands (NL); New Zealand (NZ); Nicaragua (NI); Nigeria (NG); Pakistan (PK); Peru (PE); Philippines (PH); Puerto Rico (PR); Romania (RO); Russia (RU); Serbia (RS); Singapore (SG); South Korea (KR); Taiwan ROC (TW); Tajikistan (TJ); Thailand (TH); Tunisia (TN); Turkey (TR); Ukraine (UA); United States (US); Venezuela (VE); Vietnam (VN); Zimbabwe (ZW). The sampling procedures differ from country to country; probability sample: Multistage Sample, Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2021. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Abstract

The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

Geographic coverage

13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

Analysis unit

-Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

Research Protocols:

• National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

Mode of data collection

Face-to-face [f2f]

Research instrument

A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

Cleaning operations

Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.

Explore macroeconomic statistics and indicators, including GDP, Gross Fixed Capital Formation, National Income, and more. This dataset covers a wide range of countries such as Afghanistan, Albania, Algeria, Australia, Brazil, China, Germany, India, United States, and many more.

GDP, Gross Domestic Product, Capita, GFCF, Gross Fixed Capital Formation, Value, Added, Gross, Output, National, Income, Manufacturing, Agriculture, Population, National Accounts

Afghanistan, Albania, Algeria, Andorra, 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, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Democratic Republic of the Congo, 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, Kiribati, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe

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This dataset, released August 2017, contains the top ten birthplaces of people born in non-English speaking countries, 2016. The data comprise residents of Australia who were born overseas in one of the predominantly non-English speaking countries which are in the top ten for Australia in terms of high numbers of migrants. These are, from highest to lowest: China, India, Philippines, Vietnam, Italy, Malaysia, Sri Lanka, Germany, Republic of Korea (South), and Greece. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.