This dataset provides comprehensive global population dynamics data, spanning from 1950 to 2100. It includes historical estimates and medium-scenario projections from the United Nations World Population Prospects 2024 edition. Covering 237 countries or areas, this dataset offers researchers, policymakers, and data enthusiasts a valuable resource for analyzing long-term demographic trends and their potential impacts across a 150-year period.

Key features of this dataset include:

• Annual birth and death figures for each country/area
• Historical estimates from 1950 to 2024
• Medium-scenario projections from 2024 to 2100
• Data for both sexes combined and all age groups
• Consistent methodology across countries for comparability

This dataset is ideal for:

• Long-term demographic trend analysis and forecasting
• Historical population studies and future projections
• Policy planning for healthcare, education, and social services
• Economic growth and labor force projections over extended periods
• Environmental impact studies related to population changes
• Academic research in social sciences, public health, and historical demography

Whether you're a data scientist, historian, policymaker, or social researcher, this dataset offers a wealth of information to explore and analyze global population dynamics across a century and a half.

From 1950 to 1955, the worldwide crude birth rate was just under 37 births per thousand people, which means that 3.7 percent of the population, who were alive during this time had been born in this five year period. Between this five year period, and the time between 2015 and 2020, the crude birth rate has dropped to 18.5 births per thousand people, which is fifty percent of what the birth rate was seventy years ago. This change has come as a result of increased access and reliability of contraception, a huge reduction in infant and child mortality rate, and increased educational and vocational opportunities for women. The continents that have felt the greatest change over this seventy year period are Asia and Latin America, which fell below the global average in the 1990s and early 2000s, and are estimated to have fallen below the crude birth rate of Oceania in the current five-year period. Europe has consistently had the lowest crude birth rate of all continents during the past seventy years, particularly in the 1990s and 2000s, when it fell to just over ten births per thousand, as the end of communism in Europe caused sweeping demographic change across Europe. The only continent that still remains above the global average is Africa, whose crude birth rate is fifteen births per thousand more than the world average, although the rate of decrease is higher than it was in previous decades.

Real-time world population counter with births, deaths, and demographic breakdowns

United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data was reported at 14.000 Ratio in 2015. This stayed constant from the previous number of 14.000 Ratio for 2014. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data is updated yearly, averaging 13.000 Ratio from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 15.000 Ratio in 2009 and a record low of 11.000 Ratio in 1998. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births 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: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average; This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator for monitoring maternal health.

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.

This Dataset provides comprehensive demographic information on global populations from 1950 to the present. It offers insights into various aspects of population dynamics, including population counts, gender ratios, birth and death rates, life expectancy, and migration patterns.

Column Descriptions:

SortOrder: Numeric identifier for sorting.

LocID: Location identifier.

Notes: Additional notes or comments (blank in this dataset).

ISO3_code: ISO 3-character country code.

ISO2_code: ISO 2-character country code.

SDMX_code: Statistical Data and Metadata Exchange code.

LocTypeID: Location type identifier.

LocTypeName: Location type name.

ParentID: Identifier for the parent location.

Location: Name of the location.

VarID: Identifier for the variant.

Variant: Type of population variant.

Time: Year or time period.

TPopulation1Jan: Total population on January 1st.

TPopulation1July: Total population on July 1st.

TPopulationMale1July: Total male population on July 1st.

TPopulationFemale1July: Total female population on July 1st.

PopDensity: Population density (people per square kilometer).

PopSexRatio: Population sex ratio (male/female).

MedianAgePop: Median age of the population.

NatChange: Natural change in population.

NatChangeRT: Natural change rate (per 1,000 people).

PopChange: Population change.

PopGrowthRate: Population growth rate (percentage).

DoublingTime: Time for population to double (in years).

Births: Total number of births.

Births1519: Births to mothers aged 15-19.

CBR: Crude birth rate (per 1,000 people).

TFR: Total fertility rate (average number of children per woman).

NRR: Net reproduction rate.

MAC: Mean age at childbearing.

SRB: Sex ratio at birth (male/female).

Deaths: Total number of deaths.

DeathsMale: Total male deaths.

DeathsFemale: Total female deaths.

CDR: Crude death rate (per 1,000 people).

LEx: Life expectancy at birth.

LExMale: Life expectancy for males at birth.

LExFemale: Life expectancy for females at birth.

LE15: Life expectancy at age 15.

LE15Male: Life expectancy for males at age 15.

LE15Female: Life expectancy for females at age 15.

LE65: Life expectancy at age 65.

LE65Male: Life expectancy for males at age 65.

LE65Female: Life expectancy for females at age 65.

LE80: Life expectancy at age 80.

LE80Male: Life expectancy for males at age 80.

LE80Female: Life expectancy for females at age 80.

InfantDeaths: Number of infant deaths.

IMR: Infant mortality rate (per 1,000 live births).

LBsurvivingAge1: Children surviving to age 1.

Under5Deaths: Number of deaths under age 5.

NetMigrations: Net migration rate (per 1,000 people).

CNMR: Crude net migration rate.

How to Use the Dataset:

1. Researchers can analyze demographic trends, birth and death rates, and population growth over time.
2. Policymakers can use population data to inform decisions on healthcare, education, and social services.
3. Data scientists can visualize and model population dynamics for various regions.
4. Journalists can use the dataset to report on global population trends and disparities.
5. Educators can incorporate real-world population data into lessons and research.

Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!

Real-time world population data including births, deaths, and growth rates

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

Background:

Subjected dataset is extracted using world bank and UN websites to find population collapse according to countries and regions. The code generates data for seven indicators based on the current date and is available from Year 2000 to the year 2021.

This code is useful for research purposes, there are nine distinct CSV files associated with this code, seven of them deals with indicators, one CSV file pertaining to country groups and last CSV file is analysis for 20 years between seven indicators. Below are seven indicators extracted from the world bank and the United Nations websites.

Indicators:

Total Population, Population Growth, Life Expectancy at birth, Fertility Rate, Death Rate (per 1,000 people)), Birth Rate (per 1,000 people), Median Age

Definition:

Population collapse is calculated using Total Population, Population Growth, Life Expectancy at birth, Fertility Rate, Death Rate, Birth Rate and Median Age, for that various criteria were applied to extract data:

Methodology:

The data was filtered based on several attributes, first ids and title has been extracted from the world bank data then timeframe and columns provided to extract data. This filtering process ensured that only relevant data meeting the specified criteria. For median age UN website is used and data is extracted for all countries. Median age data is not available for groups or regions; however, it could be calculated as median age data is available for all countries of the globe.

Variables: Economy, Seven Indicators Years from 2000 to 2021

For country group files, all countries are assigned according to regions, groups, by lending, by income, etc. so for this file each country is repeated as one country is member of more than one group.

Analysis:

Below screenshot is extracted for those countries whose population does fall in 20 years and death rate is increased while birth rate is decrease. So, for instance Ukraine population in Year 2002 was 48.2M while as per Year 2021 there population is decreased by 9% to 43.8M, similarly there death rate is increase from 15.7 to 18.5 (per 1000 people) and birth rate is decrease by 10% from 8.10 to 7.30.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2Ffeaff87cec8a478065eb06229045d7f1%2FPopulation%20Collapse.JPG?generation=1691841930935324&alt=media" alt="">

Kaggle Dataset Description

Title: Mortality Rate (Under-5, Per 1000 Live Births)
Subtitle: Exploring global trends in child survival and health advancements.

Detailed Description:
This dataset contains the under-5 mortality rate, measured as the number of deaths per 1,000 live births for children under five years of age. Sourced from the World Bank, it highlights progress in child survival and health outcomes globally over decades.

Key Highlights: - Annual data for countries worldwide. - Metric: Mortality rate (under-5, per 1000 live births). - Use cases: Analyze trends, compare regional disparities, and correlate mortality rates with health and economic indicators.

4. Exploratory Data Analysis (EDA)

Notebook Ideas

1. Data Cleaning:

   • Handle missing or inconsistent data.
   • Normalize data for comparison across regions.
   • Add calculated fields like regional averages or year-over-year changes.

2. Visualizations:

   • Line Graph: Trends in under-5 mortality rates over time for selected countries.
   • Heatmap: Mortality rates by region and year.
   • Scatterplot: Correlation between mortality rates and healthcare expenditure or GDP per capita.
   • Bar Chart: Top and bottom countries by under-5 mortality for a specific year.

3. Descriptive Analysis:

   • Highlight countries with the most significant reductions in mortality.
   • Analyze regional improvements over decades (e.g., Sub-Saharan Africa vs. South Asia).

5. Predictive Analysis (Optional)

• Use time-series forecasting (e.g., ARIMA or Prophet) to predict future mortality rates for specific countries or regions.
• Explore regression models to analyze the impact of factors like healthcare expenditure on mortality reduction.

6. Kaggle Notebook

Create a Kaggle notebook with: 1. Data Cleaning: Show how missing or inconsistent values are handled. 2. EDA: Include visualizations like heatmaps, scatterplots, and line charts. 3. Insights: Highlight significant findings, such as countries with notable improvements in child survival. 4. Optional Predictive Modeling: Use regression or time-series models to project future trends.

7. Call to Action

For GitHub:

• Share the GitHub repository link on LinkedIn, Twitter, and forums like Reddit (e.g., r/datascience).
• Invite collaboration:
  • "Fork this repository to add your analyses or insights!"

GitHub Link: https://github.com/yourusername/Under5_Mortality_Trends

For Kaggle:

• Encourage upvotes:
  • "If this dataset helps you, consider upvoting it to help others discover it!"
• Include questions to engage users:
  • "Which regions have made the most progress in reducing child mortality?"
  • "What correlations can be drawn between healthcare expenditure and mortality rates?"

Kaggle Link: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

8. LinkedIn Post

Post Title:
📉 Global Trends in Under-5 Mortality Rates 🌍

Post Body:
I’m excited to share my latest dataset on under-5 mortality rates (per 1,000 live births), sourced from the World Bank. This dataset highlights progress in global health and child survival, spanning decades and covering countries worldwide.

📂 Explore the Dataset:
- GitHub Repository: https://github.com/yourusername/Under5_Mortality_Trends
- Kaggle Dataset: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

Why It Matters:

Child survival is a fundamental measure of global health progress. This dataset is ideal for:
- Trend Analysis: Explore how under-5 mortality rates have evolved globally.
- Regional Comparisons: Identify disparities in child survival rates across regions.
- Correlations: Study the relationship between mortality rates and economic indicators like healthcare expenditure or GDP per capita.

📈 Get Involved:
- Use the dataset for your own analyses and visualizations.
- Share your insights and findings.
- Upvote the Kaggle dataset to help others discover it!

❓ What trends or correlations do you find in the data?
- Which country or region has shown the most improvement?
- What factors would you analyze further?

Let me know your thoughts, and feel free to share this resource with others who might benefit! 🌟

DataScience #ChildHealth #MortalityRates #WorldBankData #DataVisualization #GitHub #Kaggle #HealthAnalysis

Let me know if you'd like assistance with EDA or visualization templates!

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Demographic analysis examines and measures the dimensions and dynamics of populations; it can cover whole societies or groups defined by criteria such as education, nationality, religion, and ethnicity. Educational institutions usually treat demography as a field of sociology, though there are a number of independent demography departments. These methods have primarily been developed to study human populations, but are extended to a variety of areas where researchers want to know how populations of social actors can change across time through processes of birth, death, and migration. In the context of human biological populations, demographic analysis uses administrative records to develop an independent estimate of the population

Context

Life expectancy at birth is a key metric reflecting the average number of years a person can expect to live from birth, considering current mortality rates. Across the globe, life expectancy varies widely due to factors such as healthcare access, socio-economic conditions, and lifestyle choices. Developed nations often boast higher life expectancies, typically ranging from 75 to 85 years, owing to advanced healthcare systems and improved living standards. In contrast, developing nations often face shorter life expectancies, frequently falling below 70 years, largely due to inadequate healthcare infrastructure and prevailing socio-economic challenges. These disparities underscore the critical importance of global efforts to enhance healthcare access and address socio-economic inequalities.

Content

This dataset comprises historical information encompassing various indicators concerning Life Expectancy at Birth on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Life Expectancy at Birth from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Life Expectancy at Birth from 1990 - 2021 - Life Expectancy at Birth from year 1990 to 2021 (32 Columns.)

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/upczekR.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Freepik

Thumbnail by: Image by Quality of life icons created by Paul J. - Flaticon

Norway NO: Life Expectancy at Birth: Total data was reported at 82.510 Year in 2016. This records an increase from the previous number of 82.305 Year for 2015. Norway NO: Life Expectancy at Birth: Total data is updated yearly, averaging 76.241 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 82.510 Year in 2016 and a record low of 73.078 Year in 1963. Norway NO: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Morocco MA: Life Expectancy at Birth: Male data was reported at 74.642 Year in 2016. This records an increase from the previous number of 74.406 Year for 2015. Morocco MA: Life Expectancy at Birth: Male data is updated yearly, averaging 61.978 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 74.642 Year in 2016 and a record low of 47.152 Year in 1960. Morocco MA: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Sweden SE: Life Expectancy at Birth: Total data was reported at 82.205 Year in 2016. This stayed constant from the previous number of 82.205 Year for 2015. Sweden SE: Life Expectancy at Birth: Total data is updated yearly, averaging 77.092 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 82.254 Year in 2014 and a record low of 73.006 Year in 1960. Sweden SE: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.