Historical chart and dataset showing World life expectancy by year from 1950 to 2025.

Historical chart and dataset showing U.S. life expectancy by year from 1950 to 2025.

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

In 2024, the average life expectancy for those born in more developed countries was 76 years for men and 82 years for women. On the other hand, the respective numbers for men and women born in the least developed countries were 64 and 69 years. Improved health care has lead to higher life expectancy Life expectancy is the measure of how long a person is expected to live. Life expectancy varies worldwide and involves many factors such as diet, gender, and environment. As medical care has improved over the years, life expectancy has increased worldwide. Introduction to health care such as vaccines has significantly improved the lives of millions of people worldwide. The average worldwide life expectancy at birth has steadily increased since 2007, but dropped during the COVID-19 pandemic in 2020 and 2021. Life expectancy worldwide More developed countries tend to have higher life expectancies, for a multitude of reasons. Health care infrastructure and quality of life tend to be higher in more developed countries, as is access to clean water and food. Africa was the continent that had the lowest life expectancy for both men and women in 2023, while Oceania had the highest for men and Europe and Oceania had the highest for women.

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 ...).

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

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:

Historical chart and dataset showing Norway life expectancy by year from 1950 to 2025.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Context

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050.

Content

The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_international

https://cloud.google.com/bigquery/public-data/international-census

Dataset Source: www.census.gov

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.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What countries have the longest life expectancy?

Which countries have the largest proportion of their population under 25?

Which countries are seeing the largest net migration?

Historical chart and dataset showing Italy life expectancy by year from 1950 to 2025.

Malawi MW: Life Expectancy at Birth: Total data was reported at 63.223 Year in 2016. This records an increase from the previous number of 62.661 Year for 2015. Malawi MW: Life Expectancy at Birth: Total data is updated yearly, averaging 46.355 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 63.223 Year in 2016 and a record low of 37.805 Year in 1960. Malawi MW: 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 Malawi – Table MW.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;

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

This is an interdisciplinary data set, including longevity Indices and climate data for Japanese tree species. Please refer to Kobayashi and Akasaka (2025) Nature Ecology and Evolution for details on data collection and processing.Supplementary Data 1: Potential maximum diameter of 87 tree species.Supplementary Data 2: Life table from age 0 to the potential maximum lifespan at 1 cm diameter for 53 tree species.Supplementary Data 3: Longevity indices for 53 tree species.Supplementary Data 4: Diameter data of Carpinus laxiflora in natural forests.Supplementary Data 5: Tree monitoring data of Carpinus laxiflora across all forest types.Supplementary Code 1: R script for calculating longevity indices.Supplementary Code 2: R script for statistical analysis of the main results.

Historical chart and dataset showing Ethiopia life expectancy by year from 1950 to 2025.

There is no scientific consensus on the fundamental question whether the probability distribution of the human life span has a finite endpoint or not and, if so, whether this upper limit changes over time. Our study uses a unique dataset of the ages at death—in days—of all (about 285,000) Dutch residents, born in the Netherlands, who died in the years 1986–2015 at a minimum age of 92 years and is based on extreme value theory, the coherent approach to research problems of this type. Unlike some other studies, we base our analysis on the configuration of thousands of mortality data of old people, not just the few oldest old. We find compelling statistical evidence that there is indeed an upper limit to the life span of men and to that of women for all the 30 years we consider and, moreover, that there are no indications of trends in these upper limits over the last 30 years, despite the fact that the number of people reaching high age (say 95 years) was almost tripling. We also present estimates for the endpoints, for the force of mortality at very high age, and for the so-called perseverance parameter. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

This dataset supports the findings outlined in Regev, J., Zaar, J., Relaño-Iborra, H., and Dau, T. (2025). "Investigating the effects of age and hearing loss on speech intelligibility and amplitude modulation frequency selectivity." The Journal of the Acoustical Society of America, 157(3), 2077-2090. https://doi.org/10.1121/10.0036220The dataset contains data for 10 young and 9 older listeners with normal hearing (young and older NH listeners), as well as 9 older listeners with hearing impairment (older HI listeners).The Readme file provides a description of the dataset's content, and of the related article.The data collected were:Population data: age, tested ear, and reverse digit span scoreAudiogramsSpeech-reception thresholds (SRTs)Average dynamic ranges of masked-threshold patterns (MTPs)TP numbers across datasets (providing the corresponding TP numbers for the present dataset and those of Regev et al., 2024a and Regev et al., 2024b).Population DataDataset giving the population information for the 28 participants (tp), split into young NH, older NH, and older HI (group), tested in the study. The data summarizes the participants' age, tested ear (ear), and their Reverse Digit Span score (rds_score). The age is given in years and the reverse digit span score is given on a normalized scale from 0 to 1.The reverse digit span scores are re-used from Regev et al. (2024a; 2024b).AudiogramAudiometric thresholds (thresh) were collected for 28 participants (tp), split into young NH, older NH, and older HI (group), at frequencies (freq) of 0.125, 0.25, 0.5, 1, 2, 3, 4, 6, and 8 kHz. The thresholds are given in dB Hearing Level (HL).Speech-Reception Thresholds (SRTs)Speech-reception thresholds (SRT) at the 50%-correct point were collected 28 participants (tp), split into young NH, older NH, and older HI (group). The SRTs are given in dB signal-to-noise ratio (SNR). The test used the Danish Hearing in Noise Test (HINT; Nielsen & Dau, 2011).Five different maskers (conditions) were used:a speech-shaped noise (SSN)the ICRA-5 noise (ICRA; Dreschler et al., 2001)a male competing talker (Male comp)a female competing talker (Female comp)a cocktail-party scenario (Cocktail).A detailed description of each masker is available in the article. For each condition, the SRT was assessed twice (repetition), each time using a different list (list) from the target speech corpus. The SRTs were then averaged across repetitions.Dynamic Range of Masked-Threshold Patterns (MTPs)Regev et al. (2024a; 2024b) collected masked-threshold patterns (MTPs) for the 28 participants (tp), split into young NH, older NH, and older HI (group).MTPs were collected at four different target modulation frequencies (fmod) of 4, 16, 64, and 128 Hz.Here, the average dynamic range (dyn_range) of the MTP at the 4-Hz target modulation frequency (fmod) was derived for each participant. For each participant, the peak of the MTP was identified as the maximum threshold. The difference between the peak and the minimum threshold on each side of the peak was then computed, and the average dynamic range was finally calculated as the mean between the differences on both sides. If a single side of the peak was identified, then he threshold difference on that side was taken as the dynamic range.Masked thresholds for TP23 could not be could not be obtained for the 4-Hz target modulation frequency by Regev et al. (2024b; where the participant was labeled TP07). Hence, the dynamic range was registered as NaN.TP numbers across datasetsThe participant in this study previously provided data reported in the datasets by Regev et al. (2024a, 2024b). Some of these data were re-used in this study, either directly (i.e., the RDS scores) or to derive new measures (i.e., the MTPs to derive the dynamic ranges). This sheet provides the correspondence of the TP numbers between this dataset (tp) and those of Regev et al. (2024a, 2024b; tp_Regev_2024a and tp_Regev_2024b, respectively), for each listener group (group). The sheet states NA in case the participant was not included in the previous dataset.Ethical statementAll listeners were financially compensated for their time and gave written informed consent. Ethical approval for the study was provided by the Science-Ethics Committee for the Capital Region of Denmark (reference H-16036391).AcknowledgmentsThe authors thank Borgný Súsonnudóttir Hansen for her contribution to the data collection. The authors also thank Christian Stender Simonsen for kindly sharing the experimental framework used to run the listening test, as well as several of the signal recordings, and Jens Hjortkjær and Jonatan Märcher-Rørsted for kindly providing their implementations of the reverse digit span test and of the Cambridge formula (CamEq).ReferencesDreschler, W. A., Verschuure, H., Ludvigsen, C., & Westermann, S. (2001). ICRA Noises: Artificial Noise Signals with Speech-like Spectral and Temporal Properties for Hearing Instrument Assessment. Audiol, 40(3), 148–157. https://doi.org/10.3109/00206090109073110Nielsen J. B. & Dau T. (2011). The Danish hearing in noise test. Int J Audiol. 50(3):202-8. https://doi.org/10.3109/14992027.2010.524254Regev, J., Zaar, J.; Relaño-Iborra, H., & Dau, T. (2024a). Dataset for: "Age-related reduction of amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134527Regev, J., Relaño-Iborra, H., Zaar, J., & Dau, T.(2024b). Dataset for: "Disentangling the effects of hearing loss and age on amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134611

Large bodied species are known to live longer than small bodied species. However, it is less clear whether the positive correlation varies across taxa. In this short communication, we combine data entries from literature and databases on body mass and maximum life span for 3722 species covering taxonomic Classes Chondrichthyes, Teleostei, Amphibia, Reptilia, Aves, and Mammalia. We then analyze the log(maximum life span) – log(body mass) relationship using generalized linear model with nested random intercepts and slopes for Class/Order/Family. Our analyses generally demonstrate the positive longevity – body mass relationship but also reveal that slopes and intercepts differ slightly among all Classes except Reptilia and Amphibia. Highest slopes can be found in Classes Aves and Chondrichthyes. Differences between the smallest and largest Family-level slopes was more than threefold. While these preliminary analyses provide a brief overview of body size – longevity relationships across taxa, the dataset collated in the present study could serve as a start point for in-depth phylogenetic analyses to uncover complex pathways through which body size and its correlates might have evolved.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information

For the seventh edition (January 2025), the 2022 person weight (PWT22) was replaced with the 2024 person weight (PWT24). Only the person weight has been replaced with a 2024 version; the 2022 income weight (PIWT22) remains.

This dataset provides a comprehensive panel data structure covering 27 EU member states from 2007 to 2020. It includes economic, social, governance, and development indicators alongside data on EU funds allocated as a percentage of GDP. The primary objective of this dataset is to facilitate the analysis of the impact of EU funding on socio-economic progress across different countries and periods.The time period covered: 2007–2020Geographical coverage: 27 EU member statesTemporal resolution: Annual observationsThe dataset contains yearly observations for each country, with the following key indicators:EU Funds (EUfunds) – Ratio of annual EU funds paid to member states as a percentage of GDP (European Commission).Human Development Index (HDI) – Composite index of life expectancy, education, and income (UNDP).GINI Index (GINI) – Measure of income inequality (0 = perfect equality, 100 = maximum inequality, World Bank).Economic Sentiment Indicator (ESI ave.) – Business and consumer confidence indicator (European Commission).Economic Complexity Index (ECI) – Measure of a country’s ability to produce knowledge-intensive goods (Harvard Growth Lab).Educational Attainment (Educ.att.) – Percentage of the population with secondary or higher education (World Bank).Gender Parity Index (GPI) – Ratio of female-to-male school enrollment rates (World Bank).Index of Economic Freedom – Score based on property rights, trade freedom, and government intervention (Heritage Foundation).Governance Indicators (World Bank Governance Indicators):Control of Corruption (Contr.of.Corr.)Rule of Law (Rule.of.Law)Regulatory Quality (Regul.Qual.)Political Stability and Absence of Violence (Pol.stab.and.Abs.viol.)Voice and Accountability (Voice.and.Account.)Government Effectiveness (Gov.effect.)Sustainable Development Goals (SDG Index) – Composite measure of progress toward the UN SDGs (Sustainable Development Solutions Network).This dataset provides a rich foundation for empirical analysis, policy evaluation, and academic research on the role of EU funding in economic and social development.