In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

This statistic shows the 25 largest counties in the United States in 2022, by population. In 2022, about 9.72 million people were estimated to be living in Los Angeles County, California.

Additional information on urbanization in the United States

Urbanization is defined as the process by which cities grow or by which societies become more urban. Rural to urban migration in the United States, and around the world, is often undertaken in the search for employment or to enjoy greater access to services such as healthcare. The largest cities in the United States are steadily growing. Given their size, incremental increases yield considerable numerical gains as seen by New York increasing by 69,777 people in 2011, the most of any city. However in terms of percentage growth, smaller cities outside the main centers are growing the fastest, such as Georgetown city and Leander city in Texas.

Urbanization has increased slowly in the United States, rising from 80.77 percent of the population living in urban areas in 2010 to 82.66 percent in 2020. In 2018, the United States ranked 14th in a ranking of countries based on their degree of urbanization. Unlike fully urbanized countries such as Singapore and Hong Kong, the United States maintains a sizeable agricultural industry. Although technological developments have reduced demands for rural labor, labor in the industry and supporting services are still required.

Description

This Dataset contains details of World Population by country. According to the worldometer, the current population of the world is 8.2 billion people. Highest populated country is India followed by China and USA.

Attribute Information

• Rank : Country Rank by Population.
• Country : Name of the Country.
• Population(2024) : Current Population of each Country.
• Yearly Change : Percentage Yearly Change in Population.
• Net Change : Net change in the Population.
• Density (P/Km²) : Population density (population per square km)
• Land Area(Km²) : Total land area of the Country.
• Migrants (net) : Total number of migrants.
• Fertility Rate : Fertility rate
• Median Age : Median age of the population
• Urban Pop % : Percentage of urban population
• World Share : Share to the word with population.

Acknowledgements

https://www.worldometers.info/world-population/population-by-country/

Image by Gerd Altmann from Pixabay

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

About this dataset

Context

The dataset tabulates the population of Lost Nation by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lost Nation. The dataset can be utilized to understand the population distribution of Lost Nation by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lost Nation. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lost Nation.

Key observations

Largest age group (population): Male # 50-54 years (27) | Female # 10-14 years (25). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Lost Nation population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lost Nation is shown in the following column.
• Population (Female): The female population in the Lost Nation is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lost Nation for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lost Nation Population by Gender. You can refer the same here

The average for 2021 based on 12 countries was 25 people per square km. The highest value was in Ecuador: 72 people per square km and the lowest value was in Guyana: 4 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

This statistic shows the top 25 cities in the United States with the highest resident population as of July 1, 2022. There were about 8.34 million people living in New York City as of July 2022.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Egypt.

Analysis unit

• Household
• Individual

Universe

The WVS for Egypt covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

1- The sampling frame is the extended roaster of households for the post enumeration survey of the 2006 Census.

2- This frame covered all governorates (except the five frontiers Governorates hosting about 1.8% of the total population), within about 480 segments (average 100 HH).

3- To reduce sampling error, it was only to select 25 HH from each segment to increase the number of segments selected from each Governorate and that Number of segments was proportional to its size according to the 2006 population census.

1. Within Governorates, rural/urban parts were represented by selecting separately the number of segments proportional to its population share according to the 2006 census. Overall, a total of 122 segments were selected (out of which 56 from Urban areas and the balance from Rural areas of each Governorate), thus drawing a self-weighted sample for each Governorate based on its share of the 2006 population census.

2. The sample size was set to be 3000 individuals, to reduce sampling error and to ensure having estimates of adequate precision. The sampling unit would be the individuals 18 years old and over.

3. Both segments were selected separately from the frame of Urban/Rural area for each Governorate using systematic random sampling.

4. Households were also selected within segments (25 households from each segment) using systematic random sampling.

5. Due to rounding, the total number of segments rose to 122 segment, (and the sample size rose to 3050 individuals).

Remarks about sampling:

• The sample tended to be biased to females, as they were represented by 62% of the total sample. So weights was computed to modify the distribution of males and females, according to their distribution in the population. Basically, interviews would be conducted with the head of the household, if he/she exists and is able to answer the questionnaire. If not, another member of the household (who is 18 years or older) would answer the questionnaire. In every segment there was an alternative sample, that was also randomly selected, if an appropriate person within the household, was not available to answer the questionnaire, an alternative sampling unit would be selected. The alternative sample represented only 3.3% of the total sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

For each wave, suggestions for questions are solicited by social scientists from all over the world and a final master questionnaire is developed in English. Since the start in 1981 each successive wave has covered a broader range of societies than the previous one. Analysis of the data from each wave has indicated that certain questions tapped interesting and important concepts while others were of little value. This has led to the more useful questions or themes being replicated in future waves while the less useful ones have been dropped making room for new questions. The questionnaire is translated into the various national languages and in many cases independently translated back to English to check the accuracy of the translation. In most countries, the translated questionnaire is pre-tested to help identify questions for which the translation is problematic. In some cases certain problematic questions are omitted from the national questionnaire. WVS requires implementation of the common questionnaire fully and faithfully, in all countries included into one wave. Any alteration to the original questionnaire has to be approved by the EC. Omission of no more than a maximum of 12 questions in any given country can be allowed.

Response rate

3050 Total number of starting names/addresses 3050 - full productive interview

As of October 2025, India had the largest Instagram audience worldwide, with 480.55 million users. The United States ranked second with 181.75 million users, followed by Brazil with 147 million. However, Turkey recorded the highest audience reach, with 92.1 percent of its population using the platform. It took Instagram 11.2 years to reach the milestone of 2 billion monthly active users worldwide. Instagram’s demographics in the United States As of March 2025, Instagram was the fourth most visited social media service in the United States, after Facebook, Pinterest and X. Out of TikTok, Instagram and Snapchat, TikTok was the most used of all three platforms by Generation Z. Overall, 57 percent of Gen Z social media users used Instagram in 2021, down from 61 percent in 2020 and 64 percent in 2019. Instagram finds most popularity with those in the 25 to 34 year age group, and as of January 2025, roughly 28.3 of all users in the United States belonged to this age group. The social media app was also more likely to be used by women. Most followed accounts on Instagram Instagram’s official account had the most followers as of April 2024 with over 672 million followers. Manchester United forward Cristiano Ronaldo (@cristiano) had over 628 million followers on the platform, while the Argentinian footballer Lionel Messi (@leomessi) had over 502 million followers. The Instagram accounts of the American singer and actress Selena Gomez (@selenagomez) and the media personality and makeup mogul Kylie Jenner (@kyliejenner) had over 400 million followers each.

As of October 2025, China has the world’s largest online population, with approximately 1.3 billion internet users. India, currently the most populous nation, ranks second with about 1.03 billion users. The United States follows in third place. Worldwide internet usage As of October 2025, there are more than six billion internet users worldwide. However, user distribution varies significantly by region. In 2024, Eastern Asia alone accounted for 1.34 billion internet users, while Africa and the Middle East reported considerably lower figures. As expected, urban areas also exhibited higher rates of internet access compared to rural regions. Internet use in China It is no surprise that China ranks first among countries with the most internet users. Driven by rapid economic development and a strong cultural embrace of technology, 91.6 percent of China’s estimated 1.4 billion residents are online. As of the third quarter of 2024, about 91.8 percent of Chinese internet users were active on WeChat, the country’s most popular social platform. During the same period, Chinese internet users spent an average of five hours and 33 minutes online each day.

This statistic shows the top 25 countries in the world with the largest number of Christian population in 2010. In 2010, the United States was leading the ranking with about 243 million Christians living in the country.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.