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This dataset offers a detailed historical record of global life expectancy, covering data from 1960 to the present. It is meticulously curated to enable deep analysis of trends and gender disparities in life expectancy worldwide.

Dataset Structure & Key Columns:

Country Code (🔤): Unique identifier for each country.

Country Name (🌍): Official name of the country.

Region (🌐): Broad geographical area (e.g., Asia, Europe, Africa).

Sub-Region (🗺️): More specific regional classification within the broader region.

Intermediate Region (🔍): Additional granular geographical grouping when applicable.

Year (📅): The specific year to which the data pertains.

Life Expectancy for Women (👩‍⚕️): Average years a woman is expected to live in that country and year.

Life Expectancy for Men (👨‍⚕️): Average years a man is expected to live in that country and year.

Context & Use Cases:

This dataset is a rich resource for exploring long-term trends in global health and demography. By comparing life expectancy data over decades, researchers can:

Analyze Time Series Trends: Forecast future changes in life expectancy and evaluate the impact of health interventions over time.

Study Gender Disparities: Investigate the differences between life expectancy for women and men, providing insights into social, economic, and healthcare factors influencing these trends.

Regional & Sub-Regional Analysis: Compare and contrast life expectancy across various regions and sub-regions to understand geographical disparities and their underlying causes.

Support Public Policy Research: Inform policymakers by linking life expectancy trends with public health policies, socioeconomic developments, and other key indicators.

Educational & Data Science Applications: Serve as a comprehensive teaching tool for courses on public health, global development, and data analysis, as well as for Kaggle competitions and projects.

With its detailed, structured format and broad temporal coverage, this dataset is ideal for anyone looking to gain a nuanced understanding of global health trends and to drive impactful analyses in public health, social sciences, and beyond.

Feel free to ask for further customizations or additional details as needed!

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

National by-year life expectancy estimates for men and women, by income percentile

🇩🇪 독일

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2020 for the total, male and female populations.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2021 for the total, male and female populations.

Namur - Population - Life expectancy - Difference between women and men Difference from the average of the municipality (4.13 years) Considering that the difference in life expectancy between the two sexes should be as small as possible, Neighborhoods with a smaller difference are in a more favourable situation (in red) than those with a larger difference (in blue)

State-level life expectancy estimates for men and women, by income quartile

WMI03 - EU Life expectancy at birth and 65 by Sex. Published by Eurostat. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).EU Life expectancy at birth and 65 by Sex...

National life expectancy estimates (pooling 2001-14) for men and women, by income percentile.

There're 2 datasets:

1. Life expectancy contains information about life expectancy for men and for women, happiness score and fertility rate.
2. Suicide Rate contains information about the suicide rate and GDP per capita on each country.

Analysis of ‘Life Expectancy of the World’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/amansaxena/lifeexpectancy on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains 6 columns and 223 Rows. Each row corresponds to a country in order of their life expectancy rank. The dataset has three numeric columns, Overall Life Expectancy, Male Life Expectancy and Female Life Expectancy. The last column is Continent, which defines which continent that country lies in. This could be very well used as a class for the data.

This data can be used for classification by various techniques like SVM(linear), KNN, C.45 etc. and other supervised and unsupervised techniques.

--- Original source retains full ownership of the source dataset ---

Do women live longer than men? How long? Does it happen everywhere? Is life expectancy increasing? Everywhere? Which is the country with the lowest life expectancy? Which is the one with the highest? In this project, we will answer all these questions by manipulating and visualizing United Nations life expectancy data using ggplot2.

The dataset can be found here and contains the average life expectancies of men andwomen by country (in years). It covers four periods: 1985-1990, 1990-1995, 1995-2000, and 2000-2005.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

In 2024 life expectancy in France is a question of region, department and city

In France, life expectancy at birth is 85.3 years for women and 79.4 years for men. This means that on average, a French woman born in 2024 will live to the age of 85.3 years, and a man to the age of 79.4.

However, life expectancy varies considerably depending on the region, department and city where you live.

In region

Life expectancy is highest in Île-de-France, with 86.6 years for women and 81.9 years for men. Then come Provence-Alpes-Côte d’Azur (86.5 years for women, 81.7 years for men), Auvergne-Rhône-Alpes (86.4 years for women, 81.5 years for men) and Brittany (86.2 years for women, 81.3 years for men).

Conversely, life expectancy is lowest in Hauts-de-France, with 83.9 years for women and 78.9 years for men. Then come Normandy (84.1 years for women, 79.1 years for men), Centre-Val de Loire (84.2 years for women, 79.3 years for men) and Burgundy-Franche-Comté (84.3 years for women, 79.4 years for men).

Department

At the departmental level, the departments where we live the longest are Hauts-de-Seine (86.7 years for women, 81.9 years for men), Yvelines (86.4 years for women, 81.6 years for men), Val-de-Marne (86.3 years for women, 81.3 years for men), Paris (86.2 years for women, 81.1 years for men) and Haute-Garonne (86.2 years for women, 81.1 years for men).

Conversely, the departments where we live the least long are Creuse (76.4 years for women, 72.3 years for men), Pas-de-Calais (76.6 years for women, 72.5 years for men), Aisne (76.7 years for women, 72.6 years for men) and Somme (76.8 years for women, 72.7 years for men).

In town

At the municipal level, the cities where we live the longest are Paris (86.2 years for women, 81.1 years for men), Neuilly-sur-Seine (86.1 years for women, 81.0 years for men), Boulogne-Billancourt (85.9 years for women, 80.8 years for men), Rueil-Malmaison (85.8 years for women, 80.7 years for men) and Issy-les-Moulineaux (85.7 years for women, 80.6 years for men).

Conversely, the cities with the least long lived are The Crown (75.4 years for women, 71.3 years for men), Saint-Quentin (75.5 years for women, 71.4 years for men), Maubeuge (75.6 years for women, 71.5 years for men) and Valenciennes (75.7 years for women, 71.6 years for men).

Factors that influence life expectancy

Many factors influence life expectancy, including:

• Standard of living
• Access to care
• The conditions

To view life expectancy for a specific region, department or city, please consult the following document:

Gender differences in estimated and actual life expectancy (men’s minus women’s), 2004 and 2015.

State-level estimates of trends in life expectancy for men and women, by income quartile

Analysis of ‘U.S. State Life Expectancy by Sex, 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/53bd3de3-791d-4d5a-9897-ffe2ef288322 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2018 for the total, male and female populations.

--- Original source retains full ownership of the source dataset ---

This table shows five variants of healthy life expectancy: - Life expectancy in perceived good health - Life expectancy without physical limitations - Life expectancy without chronic diseases - Life expectancy in good mental health - Life expectancy without GALI limitations In addition, figures are also available about 'ordinary' life expectancy, so that the figures about healthy life expectancy can be related to this. In the table, the data on (healthy) life expectancy can be broken down into the following characteristics: - Gender - Age This table shows the development of healthy life expectancy over time. For example, it can be seen that life expectancy without chronic diseases for women fell in the 1980s and 1990s. Life expectancy without moderate and severe physical limitations for men actually increased over the same period. Data available from: 1981. Status of the figures: Final. Changes as of November 29, 2022 Figures for the year 2021 have been added. When will new numbers come out? Fourth quarter 2023.

This dataset provides estimates of life expectancy at birth and at 65 years of age and 95% uncertainty interval estimates by location, male, female and both sexes combined, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2016. This age-specific mortality dataset is used to enable health systems to target interventions for the older adult populations.