These data are taken from the ANNUAL datasets from the Labour Force Survey (LFS) carried out by the Office for National Statistics (ONS), providing labour market data back to 1996 for the NUTS2 areas in Wales, and back to 2001 for the local authorities in Wales. The availability of local authority data is dependent upon on an enhanced sample (around 350 per cent larger) for the annual LFS, which commenced in 2001. For years labelled 1996 to 2004 in this dataset, the actual periods covered are the 12 months running from March in the year given to February in the following year (e.g. 2001 = 1 March 2001 to 28 February 2002). Since 2004, the annual data have been produced on a rolling annual basis, updated every three months, and the dataset is now referred to as the Annual Population Survey (APS). The rolling annual averages are on a calendar basis with the first rolling annual average presented here covering the period 1 January 2004 to 31 December 2004, followed by data covering the period 1 April 2004 to 31 March 2005, with rolling quarterly updates applied thereafter. Note therefore that the consecutive rolling annual averages overlap by nine months, and there is also a two-month overlap between the last period presented on the former March to February basis, and the first period on the new basis. The population can be broken down into economically active and economically inactive populations. The economically active population is made up of persons in employment, and persons unemployed according to the International Labour Organisation (ILO) definition. This report allows the user to access these data. Although each measure is available for different population bases, there is an official standard population base used for each of the measures, as follows. Population aged 16 and over: Economic activity level, Employment level, ILO unemployment level Population aged 16-64: Economic inactivity level 16-64 population is used as the base for economic inactivity. By excluding persons of pensionable age who are generally retired and therefore economically inactive, this gives a more appropriate measure of workforce inactivity. Rates for each of the above measures are also calculated in a standard manner and are available in the dataset. With the exception of the ILO unemployment rate, each rate is defined in terms of the shares of population that fall into each category. The ILO unemployment rate is defined as ILO unemployed persons as a percentage of the economically active population. Although each rate is available for the different population bases, there is an official standard population base used for each of the rates, as follows. Percentage of population aged 16-64: Economic activity, Employment,. Economic inactivity Percentage of economically active population aged 16 and over: ILO unemployment

The indicator is defined as the percentage of the population in a given age group who are economically active. According to the definitions of the International Labour Organisation (ILO) people are classified as employed, unemployed and economically inactive for the purposes of labour market statistics. The economically active population (also called labour force) is the sum of employed and unemployed persons. Inactive persons are those who, during the reference week, were neither employed nor unemployed. The indicator is based on the EU Labour Force Survey.

Economic inactivity (aged 16 to 64 years ) by reason (seasonally adjusted). These estimates are sourced from the Labour Force Survey, a survey of households. These are official statistics in development.

The indicator is defined as the percentage of the population aged 15-64 who are economically active. According to the definitions of the International Labour Organisation (ILO) people are classified as employed, unemployed and economically inactive for the purposes of labour market statistics. The economically active population (also called labour force) is the sum of employed and unemployed persons. Inactive persons are those who, during the reference week, were neither employed nor unemployed. The indicator is based on the EU Labour Force Survey.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has 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.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

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: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Latest edition information

For the third edition (September 2023), the variables NSECM20, NSECMJ20, SC2010M, SC20SMJ, SC20SMN and SOC20M have been replaced with new versions. Further information on the SOC revisions can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

PerFemEmploy Employment to population ratio (%) of women who are of age 15 or older. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

PerFemEmployers Employers, female (% of female employment). Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Agriculture Employment in agriculture, female (% of female employment). Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

Wage.Salaried Wage and salaried workers, female (% of female employment). Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.

ContrFamWorkers Contributing family workers, female (% of female employment). Contribut...

The indicator measures the share of the economically active population aged 15 to 74 who has been unemployed for 12 months or more. Unemployed persons are defined as all persons who were without work during the reference week, were currently available for work and were either actively seeking work in the last four weeks or had already found a job to start within the next three months. The unemployment period is defined as the duration of a job search, or as the length of time since the last job was held (if shorter than the time spent on a job search). The economically active population comprises employed and unemployed persons. The indicator is part of the adjusted, break-corrected main indicators series and should not be compared with the annual and quarterly non-adjusted series, which have slightly different results.

Analysis of ‘Female Employment vs Socioeconimic Factors’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdmuhtasimbillah/female-employment-vs-socioeconimic-factors on 28 January 2022.

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

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

PerFemEmploy Employment to population ratio (%) of women who are of age 15 or older. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

PerFemEmployers Employers, female (% of female employment). Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Agriculture Employment in agriculture, female (% of female employment). Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

Wage.Salaried Wage and salaried workers, female (% of female employment). Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.

ContrFamWorkers Contributing family workers, female (% of female employment). Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.

OwnAccount Own-account female workers (% of employment). Own-account workers are workers who, working on their own account or with one or more partners, hold the types of jobs defined as "self-employment jobs" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of "self-employed".

Vulnerable Vulnerable employment, female (% of female employment). Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.

Inspiration

Linear model as well as other statistical methods can be applied on this dataset to analyze if there is any viable relationship between the predictor and the response variables.

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

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Afghanistan Employment To Population Ratio: Modeled ILO Estimate: Aged 15+ data was reported at 32.511 % in 2024. This records an increase from the previous number of 32.402 % for 2023. Afghanistan Employment To Population Ratio: Modeled ILO Estimate: Aged 15+ data is updated yearly, averaging 42.916 % from Dec 1991 (Median) to 2024, with 34 observations. The data reached an all-time high of 43.389 % in 1991 and a record low of 32.333 % in 2022. Afghanistan Employment To Population Ratio: Modeled ILO Estimate: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Afghanistan – Table AF.World Bank.WDI: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.;International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

This table contains data at regional level on the number of persons employed on agricultural holdings, the corresponding annual work units (AWUs) and the number of holdings with workers.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Data on labour force refer to the period April to March of the year preceding the agricultural census.

In 2022, equidae are not part of the Agricultural Census. This affects the farm type and the total number of farms in the Agricultural Census. Farms with horses, ponies and donkeys that were previously classified as ‘specialist grazing livestock' could be classified, according to their dominant activity, as another farm type in 2022.

From 2018 onwards the number of calves for fattening, pigs for fattening, chicken and turkey are adjusted in the case of temporary breaks in the production cycle (e.g. sanitary cleaning). The agricultural census is a structural survey, in which adjustment for temporary breaks in the production cycle is a.o. relevant for the calculation of the economic size of the holding, and its farm type. In the livestock surveys the number of animals on the reference day is relevant, therefore no adjustment for temporary breaks in the production cycle are made. This means that the number of animals in the tables of the agricultural census may differ from those in the livestock tables (see ‘links to relevant tables and relevant articles).

From 2017 onwards, animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of the combined data collection. The I&R registers are the responsibility of RVO (Netherlands Enterprise Agency). Since 2017, cattle numbers are derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly at RVO. Poultry data is collected via the designated database Poultry Information System Poultry (KIP) from Avined. Avined is a branch organization for the egg and poultry meat sectors. Avined passes the data on to the central database of RVO. Due to the transition to the use of I&R registers, a change in classification will occur for sheep and goats from 2018 onwards.

Since 2016, information of the Dutch Business Register is used to define the agricultural census. Registration in the Business Register with an agricultural standard industrial classification code, related to NACE/ISIC, (in Dutch SBI: ‘Standaard BedrijfsIndeling’) is leading to determine whether there is an agricultural holding. This aligns the agricultural census as closely as possible to the statistical regulations of Eurostat and the (Dutch) implementation of the definition of 'active farmer' as described in the common agricultural policy.

The definition of the agricultural census based on information from the Dutch Business Register mainly affects the number of holdings, a clear deviation of the trend occurs. The impact on areas (except for other land and rough grazing) and the number of animals (except for sheep, and horses and ponies) is limited. This is mainly due to the holdings that are excluded as a result of the new delimitation of agricultural holdings (such as equestrian centres, city farms and organisations in nature management).

In 2011 there were changes in geographic assignment of holdings with a foreign main seat. This may influence regional figures, mainly in border regions.

Until 2010 the economic size of agricultural holdings was expressed in Dutch size units (in Dutch NGE: 'Nederlandse Grootte Eenheid'). From 2010 onwards this has become Standard Output (SO). This means that the threshold for holdings in the agricultural census has changed from 3 NGE to 3000 euro SO. For comparable time series the figures for 2000 up to and including 2009 have been recalculated, based on SO coefficients and SO typology. The latest update was in 2016.

Data available from: 2000

Status of the figures: The figures are final.

Changes as of March 28, 2025: the final figures for 2024 have been added.

When will new figures be published? According to regular planning provisional figures for the current year are published in November and the definite figures will follow in March of the following year.

Dataset population: Persons aged 16 to 74

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Means of travel to work

The means of travel used for the longest part, by distance, of the usual journey to work. This topic is only applicable to people who were in employment in the week before the census.

'Public transport' and 'car or van availability' are a different statistic to the 2001 Census.

About this dataset

Context

The dataset tabulates the data for the Lambert, MO population pyramid, which represents the Lambert population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Lambert, MO, is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Lambert, MO, is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Total dependency ratio for Lambert, MO is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Potential support ratio, which is the number of youth (working age population) per elderly, for Lambert, MO is 0.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lambert Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Kupreanof, AK population pyramid, which represents the Kupreanof population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Kupreanof, AK, is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Kupreanof, AK, is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Total dependency ratio for Kupreanof, AK is not applicable, as there were no residents in the working age population range according to the 2018-2022 ACS 5-Year estimates..
• Potential support ratio, which is the number of youth (working age population) per elderly, for Kupreanof, AK is 0.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Kupreanof Population by Age. You can refer the same here