CONTENT

The US Census Bureau's world population clock estimated that the global population as of September 2022 was 7,922,312,800 people and was expected to reach 8 billion by mid-November of 2022. This total far exceeds the 2015 world population of 7.2 billion. The world's population continues to increase by roughly 140 people per minute, with births outweighing deaths in most countries.

Overall, however, the rate of population growth has been slowing for several decades. This slowdown is expected to continue until the rate of population growth reaches zero (an equal number of births and deaths) around 2080-2100, at a population of approximately 10.4 billion people. After this time, the population growth rate is expected to turn negative, resulting in global population decline.

Countries with more than 1 billion people China is currently the most populous country in the world, with a population estimated at more than 1.42 billion as of September 2022. Only one other country in the world boasts a population of more than 1 billion people: India, whose population is estimated to be 1.41 billion people—and rising.

Context The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Have you ever wondered how the population landscape of our planet looks in 2025? This dataset brings together the latest population statistics for 233 countries and territories, carefully collected from Worldometers.info — one of the most trusted global data sources.

📊 It reveals how countries are growing, shrinking, and evolving demographically. From population density to fertility rate, from migration trends to urbanization, every number tells a story about humanity’s future.

🌆 You can explore which nations are rapidly expanding, which are aging, and how urban populations are transforming global living patterns. This dataset includes key metrics like yearly population change, net migration, land area, fertility rate, and each country’s share of the world population.

🧠 Ideal for data analysis, visualization, and machine learning, it can be used to study global trends, forecast population growth, or build engaging dashboards in Python, R, or Tableau. It’s also perfect for students and researchers exploring geography, demographics, or development studies.

📈 Whether you’re analyzing Asia’s population boom, Europe’s aging curve, or Africa’s youthful surge — this dataset gives you a complete view of the world’s demographic balance in 2025. 🌎 With 233 rows and 12 insightful columns, it’s ready for your next EDA, visualization, or predictive modeling project.

🚀 Dive in, explore the data, and uncover what the world looks like — one country at a time.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

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

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

Population of Urban Agglomerations with 300,000 Inhabitants or more in 2014, by city, 1950-2030 (thousands). Data for 1,692 cities contained in the Excel file. Note: Each country has its own definition of what is 'urban' and therefore use exercise caution when comparing cities in different countries. Data available from the United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, CD-ROM Edition. Further detail of population estimates, land area, and population density for world urban areas with over 500,000 people (924 areas) is available with Demographia's World Urban Areas report (2014). Much of this data is based on the UN urban agglomerations, though a range of other sources are also used.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Content

The dataset has 6 columns described as following:

Rank: Country rank by population

Country: Country name

Region: Country region

Population: Country population

Percentage: Percentage of population worldwide

Date: Date when population was measured

Questions to be answered

What is the population of each region ? Which country has the most population in each region ? What is the percentage of the first 10 countries ?

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS).

The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided.

Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed.

Four files are available for download:

• Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level.
• Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole
• Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided.
• Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data.

An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015.

Nationality data can be found here: https://data.london.gov.uk/dataset/nationality

Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

The Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 is a raster representation of nation-states in GPWv4 for use in aggregating population data. This data set was produced from the input census units which were used to create a raster surface where pixels that cover the same census data source (most often a country or territory) have the same value. Note that these data are not official representations of country boundaries; rather, they represent the area covered by the input data. In cases where multiple countries overlapped a given pixel (e.g. on national borders), the pixels were assigned the country code of the input data set which made up the majority of the land area. The data file was produced as a global raster at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions. Each level of aggregation results in the loss of one or more countries with areas smaller than the cell size of the final raster. Rasters of all resolutions were also converted to polygon shapefiles. To provide a raster representation of nation-states in GPWv4 for use in aggregating population data.

The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

Data Usage Notice

This version is not recommended for download. Please check the newest version.

We would like to inform you that the updated GlobPOP dataset (2021-2022) have been available in version 2.0. The GlobPOP dataset (2021-2022) in the current version is not recommended for your work. The GlobPOP dataset (1990-2020) in the current version is the same as version 1.0.

Thank you for your continued support of the GlobPOP.

If you encounter any issues, please contact us via email at lulingliu@mail.bnu.edu.cn.

Introduction

Continuously monitoring global population spatial dynamics is essential for implementing effective policies related to sustainable development, such as epidemiology, urban planning, and global inequality.

Here, we present GlobPOP, a new continuous global gridded population product with a high-precision spatial resolution of 30 arcseconds from 1990 to 2020. Our data-fusion framework is based on cluster analysis and statistical learning approaches, which intends to fuse the existing five products(Global Human Settlements Layer Population (GHS-POP), Global Rural Urban Mapping Project (GRUMP), Gridded Population of the World Version 4 (GPWv4), LandScan Population datasets and WorldPop datasets to a new continuous global gridded population (GlobPOP). The spatial validation results demonstrate that the GlobPOP dataset is highly accurate. To validate the temporal accuracy of GlobPOP at the country level, we have developed an interactive web application, accessible at https://globpop.shinyapps.io/GlobPOP/, where data users can explore the country-level population time-series curves of interest and compare them with census data.

With the availability of GlobPOP dataset in both population count and population density formats, researchers and policymakers can leverage our dataset to conduct time-series analysis of population and explore the spatial patterns of population development at various scales, ranging from national to city level.

Data description

The product is produced in 30 arc-seconds resolution(approximately 1km in equator) and is made available in GeoTIFF format. There are two population formats, one is the 'Count'(Population count per grid) and another is the 'Density'(Population count per square kilometer each grid)

Each GeoTIFF filename has 5 fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below with the example filename:

GlobPOP_Count_30arc_1990_I32

Field 1: GlobPOP(Global gridded population)
Field 2: Pixel unit is population "Count" or population "Density"
Field 3: Spatial resolution is 30 arc seconds
Field 4: Year "1990"
Field 5: Data type is I32(Int 32) or F32(Float32)

More information

Please refer to the paper for detailed information:

Liu, L., Cao, X., Li, S. et al. A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning. Sci Data 11, 124 (2024). https://doi.org/10.1038/s41597-024-02913-0.

The fully reproducible codes are publicly available at GitHub: https://github.com/lulingliu/GlobPOP.

This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Two files are available to download: Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole. A Tableau visualisation tool is also available. Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

About this dataset

Context

The dataset tabulates the Country Club population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Country Club. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,811 (57.11% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Country Club population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Country Club is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Country Club is shown in the following column.

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 Country Club Population by Age. You can refer the same here

European countries are experiencing population decline and the tacit assumption in most analyses is that the decline may have detrimental welfare effects. In this paper we use a survey among the population in the Netherlands to discover whether population decline is always met with fear. A number of results stand out: population size preferences differ by geographic proximity: at a global level the majority of respondents favors a (global) population decline, but closer to home one supports a stationary population. Population decline is clearly not always met with fear: 31 percent would like the population to decline at the national level and they generally perceive decline to be accompanied by immaterial welfare gains (improvement environment) as well as material welfare losses (tax increases, economic stagnation). In addition to these driving forces it appears that the attitude towards immigrants is a very strong determinant at all geographical levels: immigrants seem to be a stronger fear factor than population decline.

About this dataset

Context

The dataset tabulates the data for the Hill Country Village, TX population pyramid, which represents the Hill Country Village population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Hill Country Village, TX, is 22.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Hill Country Village, TX, is 49.0.
• Total dependency ratio for Hill Country Village, TX is 71.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Hill Country Village, TX is 2.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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

Variables / Data Columns

• Age Group: This column displays the age group for the Hill Country Village population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hill Country Village for the selected age group is shown in the following column.
• Population (Female): The female population in the Hill Country Village for the selected age group is shown in the following column.
• Total Population: The total population of the Hill Country Village for the selected age group is shown in the following column.

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 Hill Country Village Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Country Club Hills population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Country Club Hills. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 10,026 (59.87% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Country Club Hills population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Country Club Hills is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Country Club Hills is shown in the following column.

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 Country Club Hills Population by Age. You can refer the same here

The mid-year estimates refer to the population on 30 June of the reference year and are produced in line with the standard United Nations (UN) definition for population estimates. They are the official set of population estimates for the UK and its constituent countries, the regions and counties of England, and local authorities and their equivalents.