About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 United States Population by Age. You can refer the same here

This dataset provides comprehensive information about the employment-to-population ratio and the actual population in the United States, spanning from 1979 to 2022.

The employment-to-population ratio signifies the percentage of the civilian noninstitutional population that is employed.

Interesting Task Ideas:

1. Take a closer look at how many people are employed compared to the total population, and see how it relates to different stuff like education, gender, race, and age groups.
2. Look into how employment has changed over time for specific groups of people, like women, different age groups, and different races or ethnicities.
3. See if there's any connection between the employment-to-population ratio and economic stuff like how much the country's making (GDP growth) or how many people are out of work (unemployment rates)
4. Find out if education levels have anything to do with how many people are employed in different racial and ethnic groups.
5. Dive into what happens to employment rates during tough times like a recession or when the economy is doing well.
6. Use the dataset to make predictions about what might happen with employment in the future.

If you find this dataset useful, please consider giving it an upvote! 😊💝

Checkout my other datasets

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150K TMDb TV Shows

900K TMDb Movies

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Column Descriptions

About this dataset

Context

The dataset tabulates the data for the Maryland population pyramid, which represents the Maryland 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 Maryland, is 28.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Maryland, is 24.9.
• Total dependency ratio for Maryland is 53.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Maryland is 4.0.

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 Maryland population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Maryland for the selected age group is shown in the following column.
• Population (Female): The female population in the Maryland for the selected age group is shown in the following column.
• Total Population: The total population of the Maryland 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 Maryland Population by Age. You can refer the same here

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

United Nations Development Program data for 'Demography' dimension, with the following indicators- -Median age - Population under age 5 - Population between age 15-64 -Population above age 65 -Sex Ration -Total Population -Urban Population -Young age dependency ratio -Old age dependency ration

There are some countries who have data which do not have internationally comparable data, and they do not have a numerical rank in the HDI Rank column, instead having '..' . Some countries do not have data published for them but have an HDI rank, having '..' in columns with missing values. This data is for 2019, which is the latest data published by UNDP.

this graph was created in R,PowerBi and Tableau:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1ff6f4c9909fbc1f9823a40b599a42e1%2Fgraph1.gif?generation=1725724753823963&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F2fe80fc1639fd390ce2b3da72bc9686c%2Fgraph2.jpg?generation=1725724760373919&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fe621d0a637c3d5c83825a69de684d8c5%2Fgraph3.png?generation=1725724765816050&alt=media" alt="">

The elderly population refers to the portion of a country's inhabitants who are aged 65 and older. This demographic plays a crucial role in various economic and social analyses, especially when it comes to determining the dependent population. The dependent population consists of those individuals who do not actively participate in the workforce and, as a result, rely on others for essential goods and services. This group primarily includes both the elderly and the youth (typically under 15 years of age).

The concept of the elderly dependency ratio is a significant measure used to understand the burden on the working-age population, which consists of those between the ages of 15 and 64. This ratio is calculated by comparing the number of elderly people to those of working age. A higher elderly dependency ratio indicates a larger proportion of elderly individuals relative to those who are contributing economically, leading to increased demands on social systems such as healthcare, pensions, and other support services.

These demographic shifts have widespread implications for both government policies and private sectors. As the elderly population increases, so too does the pressure on pension systems and healthcare services, necessitating reforms to ensure sustainability. Additionally, the aging population affects broader economic growth and welfare, as fewer people of working age contribute to economic productivity, potentially slowing overall economic expansion.

This indicator, often measured as a percentage of the total population, provides valuable insights into the aging trends within a society and their potential impact on the economy, welfare, and social structures. Understanding these trends is essential for shaping future policies that address the needs of an aging population while maintaining economic stability and growth.

About this dataset

Context

The dataset tabulates the North Carolina population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of North Carolina. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 6.47 million (61.17% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the North Carolina population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in North Carolina is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the North Carolina 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 North Carolina Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Raleigh, NC population pyramid, which represents the Raleigh 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 Raleigh, NC, is 23.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Raleigh, NC, is 16.4.
• Total dependency ratio for Raleigh, NC is 40.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Raleigh, NC is 6.1.

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 Raleigh population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Raleigh for the selected age group is shown in the following column.
• Population (Female): The female population in the Raleigh for the selected age group is shown in the following column.
• Total Population: The total population of the Raleigh 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 Raleigh Population by Age. You can refer the same here

This dataset contains Iowa population estimate of individuals 16 years and older by sex, age, and employment status for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B23025. Sex includes the following: Both, Male and Female. Age includes the following: All Age Groups, 16 to 19 years, 20 and 21 years, 22 to 24 years, 25 to 29 years, 30 to 34 years, 35 to 44 years, 45 to 54 years, 55 to 59 years, 60 and 61 years, 62 to 64 years, 65 to 69 years, 70 to 74 years, and 75 years and older. Employment status includes the following: All Employment Statuses, Labor Force, Civilian Labor Force, Civilian Employed Labor Force, Civilian Unemployed Labor Force, Armed Forces, and Not in Labor Force.

Proportion of working age (19 years to retirement age) population qualified to at least level 3 or higher. People are counted as being qualified to level 3 or above if they have achieved either at least 2 A-levels grades A-E, 4 A/S levels graded A-E, or any equivalent (or higher) qualification in the Qualifications and Credit Framework. Age group; 19 to 59 inclusive for women and 19-64 inclusive for men

About this dataset

Context

The dataset tabulates the Virginia population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Virginia. The dataset can be utilized to understand the population distribution of Virginia by age. For example, using this dataset, we can identify the largest age group in Virginia.

Key observations

The largest age group in Virginia was for the group of age 30 to 34 years years with a population of 596,257 (6.89%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Virginia was the 85 years and over years with a population of 148,515 (1.72%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

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 in consideration
• Population: The population for the specific age group in the Virginia is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Virginia total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Virginia Population by Age. You can refer the same here

Statistics Population of Namur - Major age groups with distinction Men and Women by neighbourhood. Indicator: Ageing coefficient (Ratio of the population not of working age, i.e. young people aged 0-19 and people aged 65 and over to the population of working age (people aged 20-64). This coefficient expressed as a percentage may exceed 100% and, in this case, the number of persons considered to be dependent is greater than the number of persons in employment. Formula: Total 0 to 19 years + 65 years and + / Population aged 20 to 64) - Figures collected on 1 January of each year since 1985. This dataset is used on the Portal "Statistics of the 46 districts of Namur", tab Demographic Observatory of the OPENDATA of the municipality of Namur.

Context

This dataset is based on the original SpaceNet 7 dataset, with a few modifications.

Content

The original dataset consisted of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The original dataset will comprised over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations.

This dataset builds upon the original dataset, such that each image is segmented into 64 x 64 chips, in order to make it easier to build a model for.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F66851650dbfb7017f1c5717af16cea3c%2Fchips.png?generation=1607947381793575&alt=media" alt="">

The images also compare the changes that between each image of each month, such that an image taken in month 1 is compared with the image take in month 2, 3, ... 24. This is done by taking the cartesian product of the differences between each image. For more information on how this is done check out the following notebook.

The differences between the images are captured in the output mask, and the 2 images being compared are stacked. Which means that our input images have dimensions of 64 x 64 x 6, and our output mask has dimensions 64 x 64 x 1. The reason our input images have 6 dimensions is because as mentioned earlier, they are 2 images stacked together. See image below for more details:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F9cdcf8481d8d81b6d3fed072cea89586%2Fdifference.png?generation=1607947852597860&alt=media" alt="">

The image above shows the masks for each of the original satellite images and what the difference between the 2 looks like. For more information on how the original data was explored check out this notebook.

Data Structure

The data is structured as follows:
chip_dataset
└── change_detection
└── fname
├── chips
│ └── year1_month1_year2_month2
│ └── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname.tif
└── masks
└── year1_month1_year2_month2
└── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname_blank.tif

The _blank in the mask chips, indicates whether the mask is a blank mask or not.

For more information on how the data was structured and augmented check out the following notebook.

Acknowledgements

All credit goes to the team at SpaceNet for collecting and annotating and formatting the original dataset.

Labour Force Survey summary data, including employment, unemployment and economic inactivity levels and rates, UK, rolling three-monthly figures published monthly, non-seasonally adjusted. These are official statistics in development.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Ratio between the number of older people (over 65) and the number of people of working age (20-64) in 2016 (Lorraine: 2014, Luxembourg: 2017) Territorial entities: Cantons (LOR, LUX), Kreise (RLP, SL), arrondissements (WAL) Statistical data sources: INSEE Grand Est, Statistisches Landesamt Rheinland-Pfalz, Statistisches Amt Saarland, STATEC, IWEPS. Calculations: LISER 2018 Geodata sources: ACT Luxembourg 2017, IGN France 2017, GeoBasis-DE / BKG 2017, NGI-Belgium 2017. Harmonization: SIG-GR / GIS-GR 2018

This shows the proportion of the population aged 16 to 64 qualified to National Vocational Qualification (NVQ) level 1 or above. People are counted as being qualified to level 1 and above if they have achieved at least NVQ level 1 or any equivalent or higher qualification in the Qualifications and Credit Framework. Data are from the Annual Population Survey (APS) which is a continuous household survey, covering the UK with a sample size of approximately 320,000 respondents. The data sets consist of 12 months of survey data and are broken down on a quarterly basis, qualification data is only available for calendar year survey periods, i.e. periods ending in December.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

About this dataset

Context

The dataset tabulates the data for the Texas population pyramid, which represents the Texas 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 Texas, is 31.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Texas, is 20.0.
• Total dependency ratio for Texas is 51.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Texas is 5.0.

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 Texas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Texas for the selected age group is shown in the following column.
• Population (Female): The female population in the Texas for the selected age group is shown in the following column.
• Total Population: The total population of the Texas 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 Texas Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Barberton, OH population pyramid, which represents the Barberton 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 Barberton, OH, is 30.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Barberton, OH, is 32.2.
• Total dependency ratio for Barberton, OH is 62.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Barberton, OH is 3.1.

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 Barberton population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Barberton for the selected age group is shown in the following column.
• Population (Female): The female population in the Barberton for the selected age group is shown in the following column.
• Total Population: The total population of the Barberton 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 Barberton Population by Age. You can refer the same here