Annual estimates of paid hours worked, weekly, hourly and annual earnings for UK employees by sex, and full-time and part-time, by region and four-digit Standard Occupational Classification.

Office for National Statistics’ national and subnational mid-year population estimates for England and Wales for a selection of administrative and census areas by additional useful age for 2012 to 2020. Age categories include: 0-15, 5-11, 11-15, 16-17, 16-29, 16-64, 18-24, 30-44, 45-64, 65+ & 70+. The data is source is from ONS Population Estimates. Find out more about this dataset here.

This data is issued at (BGC) Generalised (20m) boundary type for:

Country, Region, Upper Tier Local Authority (2021), Lower Tier Local Authority (2021), Middle Super Output Area (2011), and Lower Super Output Area (2011).

If you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at dataenquiries@esriuk.com.

The Office for National Statistics (ONS) produces annual estimates of the resident population of England and Wales at 30 June every year. The most authoritative population estimates come from the census, which takes place every 10 years in the UK. Population estimates from a census are updated each year to produce mid-year population estimates (MYEs), which are broken down by local authority, sex and age. More detailed information on the methods used to generate the mid-year population estimates can be found here.

For further information on the usefulness of the data and guidance on small area geographies please see here.The currency of this data is 2021.

Methodology

The total and 5-year breakdown population counts are reproduced directly from the source data. The age range estimates have been calculated from the published estimates by single year of age. The percentages are calculated using the gender specific (total, female or male) total population count as a denominator except in the case of the male and female total population where the total population is used to give female and male proportions.

This dataset will be updated annually, in two releases.

Creator: Office for National Statistics. Aggregated age groupings and percentages calculated by Esri UK._The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Indicators from the GP Access Survey related to people’s experiences of NHS waiting times in England.

All persons aged 15 and over, time spent walking on a typical day by Levels of Deprivation, Year and Statistic

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Annual data from the Crime Survey for England and Wales (CSEW). Data include when and where incidents happened, information about offenders, the victim's perception of the incident, and who they reported the incident to.

IH152 - All persons aged 15 and over, time spent walking on a typical day. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).All persons aged 15 and over, time spent walking on a typical day...

In 2013, the total number of women aged 15 to 49 years worldwide was *** billion. In 2017 it was estimated that this number would increase to almost ****billion by 2025. The U.S. accounted for a small proportion of the total number of women globally in 2013 with just **** million. Global demographics In 2024, the total global population at approximately **** billion people. In 2024, the continent with the largest proportion of the global population was Asia, followed by Africa. While North America and Oceania were some of the least populated areas of the world. The age distribution of the population varies by region as well. For example, the percentage of the global population between the ages of 15 and 64 years varies between ** percent and ** percent. Women’s health worldwide Women face different health challenges depending on the region and country. One important global health issue is maternal mortality. The country with the highest maternal mortality rate in 2023 was Nigeria. Chad had the seventh-highest estimated birth rate in 2024 and was the country with the second-highest maternal mortality rate. The United States has one of the highest maternal mortality rates when compared to similarly developed countries.

Indicators from the GP Access Survey related to people’s experiences of GP practice access in England.

Lower quartile price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year.

DEFINITIONS

Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate.
Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state.
Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.

NOTES

Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5).

Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.

Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4).

The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that
1-α≤P({C│y})=∫p{θ │y}dθ,
where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6).

County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7).

SOURCES

National Center for Health Statistics. Vital statistics data available online, Natality all-county files. Hyattsville, MD. Published annually.

For details about file release and access policy, see NCHS data release and access policy for micro-data and compressed vital statistics files, available from: http://www.cdc.gov/nchs/nvss/dvs_data_release.htm.

For natality public-use files, see vital statistics data available online, available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

National Center for Health Statistics. U.S. Census populations with bridged race categories. Estimated population data available. Postcensal and intercensal files. Hyattsville, MD. Released annually.

For population files, see U.S. Census populations with bridged race categories, available from: https://www.cdc.gov/nchs/nvss/bridged_race.htm.

REFERENCES

1. Khan D, Rossen LM, Hamilton B, Dienes E, He Y, Wei R. Spatiotemporal trends in teen birth rates in the USA, 2003–2012. J R Stat Soc A 181(1):35–58. 2017. Available from: http://onlinelibrary.wiley.com/doi/10.1111/rssa.12266/abstract.

2. Khan D, Rossen LM, Hamilton BE, He Y, Wei R, Dienes E. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012. Spat Spatiotemporal Epidemiol 21:67–75. 2017. Available from: http://www.sciencedirect.com/science/article/pii/S1877584516300442.

3. Rue H, Martino S, Lindgren F. INLA: Functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation. R package, version 0.0. 2009.

4. Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc B 71(2):319–92. 2009.

5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Mathews TJ. Births: Final data for 2015. National Vital Statistics Reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf (1.9 MB).

6. Carlin BP, Louis TA. Bayesian methods for data analysis. Boca Raton, FL: CRC Press, 2009.

7. National Center for Health Statistics. County geography changes: 1990–2012. Available from: http://www.cdc.gov/nchs/data/nvss/bridged_race/County_Geography_Changes.pdf (39 KB).

Analysis of ‘C1215 - Population At Work Aged 15 Years and Over Usually Resident in the State and Present in their Usual Residence on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/8430a14e-1481-4ec4-9a96-e14b5c78d85f on 17 January 2022.

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

Population At Work Aged 15 Years and Over Usually Resident in the State and Present in their Usual Residence on Census Night

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

The activity rate expresses the ratio to the working-age population (15-64) of people who actually enter the labour market, whether they are employed or unemployed. That rate therefore reflects behaviour in relation to the labour market, which is itself a function of a considerable number of variables relating as much to the individual, his family and his culture as to the economic and institutional context in which he operates. See also: - on our website 'Labour Market Statistics', The Employment Accounts and the IWEPS Working Paper No 13. Note: From 2011, the indicators are calculated on the basis of Steunpunt Werk estimates, which showed a break in series in 2017: the methodology for estimating non-taxable students is changed and employees of international organisations have been included in the employed assets. In 2019, the source used by Steunpunt Werk for the number of outgoing cross-border commuters changes, which leads to a drop in employment, and therefore also in activity, and an increase in the unemployment rate, which can be significant in some border municipalities. As a result of this problem and the delay in Steunpunt Werk’s estimates due to the increasing difficulty of obtaining sufficiently detailed data on employed workers, from 2019 the indicators are calculated on the basis of provisional estimates from IWEPS. More information on the IWEPS website: - the "\2" - the "\2" - labour market statistics

Le taux d’activité exprime le rapport à la population d’âge actif (15 à 64 ans) des personnes qui se présentent effectivement sur le marché du travail, qu’elles soient occupées ou chômeuses. Ce taux traduit donc un comportement par rapport au marché du travail comportement qui est lui-même fonction d’un nombre considérable de variables tenant autant à l’individu, à sa famille et à sa culture, qu’au contexte économique et institutionnel dans lequel il évolue. Voir aussi : - sur notre site ’Statistiques – Marché du travail', Les comptes de l’emploi et le Working Paper de l’IWEPS n°13. Note : A partir de 2011, les indicateurs sont calculés sur la base des estimations du Steunpunt Werk, marquées par une rupture de série en 2017 : la méthode d'estimation des étudiants non assujettis est modifiée et les salariés d'organismes internationaux ont été intégrés aux actifs occupés. En 2019, la source utilisée par le Steunpunt Werk pour le nombre de frontaliers sortants change, ce qui entraine une baisse de l’emploi, donc aussi de l’activité, et une hausse du taux de chômage qui peuvent être importantes dans certaines communes frontalières. Suite à ce problème et au retard des estimations du Steunpunt Werk du à la difficulté croissante d’obtenir des données suffisamment détaillées sur les travailleurs salariés, à partir de 2019, les indicateurs sont calculés sur la base d’estimations provisoires de l’IWEPS. Plus d'informations sur le site de l'IWEPS : - la "\2" - les "\2" - les statistiques du marché du travail

The activity rate expresses the ratio to the working-age population (15-64) of people who actually enter the labour market, whether they are employed or unemployed. That rate therefore reflects behaviour in relation to the labour market, which is itself a function of a considerable number of variables relating as much to the individual, his family and his culture as to the economic and institutional context in which he operates. See also: - on our website 'Labour Market Statistics', The Employment Accounts and the IWEPS Working Paper No 13. Note: From 2011, the indicators are calculated on the basis of Steunpunt Werk estimates, which showed a break in series in 2017: the methodology for estimating non-taxable students is changed and employees of international organisations have been included in the employed assets. In 2019, the source used by Steunpunt Werk for the number of outgoing cross-border commuters changes, which leads to a drop in employment, and therefore also in activity, and an increase in the unemployment rate, which can be significant in some border municipalities. As a result of this problem and the delay in Steunpunt Werk’s estimates due to the increasing difficulty of obtaining sufficiently detailed data on employed workers, from 2019 the indicators are calculated on the basis of provisional estimates from IWEPS. More information on the IWEPS website: - the "\2" - the "\2" - labour market statistics

Table of INEBase Summary of data on Children aged 10-15 by autonomous communities and cities, gender, size of household, type of household, place of residence, net monthly household income and main variables. Autonomous City and Community. Survey on Equipment and Use of Information and Communication Technologies in Households