About this dataset

Context

The dataset tabulates the Economy population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Economy across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Economy was 8,962, a 0.18% decrease year-by-year from 2022. Previously, in 2022, Economy population was 8,978, a decline of 0.74% compared to a population of 9,045 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Economy decreased by 452. In this period, the peak population was 9,414 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Economy is shown in this column.
• Year on Year Change: This column displays the change in Economy population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Economy Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Economy by race. It includes the distribution of the Non-Hispanic population of Economy across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Economy across relevant racial categories.

Key observations

With a zero Hispanic population, Economy is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 122 (97.60% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/economy-in-population-by-race-and-ethnicity.jpeg" alt="Economy Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Economy
• Population: The population of the racial category (for Non-Hispanic) in the Economy is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Economy total Non-Hispanic 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 Economy Population by Race & Ethnicity. You can refer the same here

This dataset provides a historical overview of key global indicators, including Gross Domestic Product (GDP), population growth, and CO2 emissions. It captures economic trends, demographic shifts, and environmental impacts over multiple decades, making it useful for researchers, analysts, and policymakers.

The dataset includes Real GDP (inflation-adjusted), allowing for economic trend analysis while accounting for inflation effects. Additionally, it incorporates CO2 emissions data, enabling studies on the relationship between economic growth and environmental impact.

This dataset is valuable for multiple research areas:

✅ Macroeconomic Analysis – Study global economic growth, recessions, and recovery trends.

✅ Inflation & Monetary Policy – Compare nominal vs. real GDP to assess inflationary trends.

✅ Climate Change Research – Analyze CO2 emissions alongside economic growth to identify sustainability challenges.

✅ Predictive Modeling – Train machine learning models for forecasting GDP, population, or emissions.

✅ Public Policy & Development – Evaluate the impact of economic and environmental policies over time.

This dataset is shared for educational and analytical purposes only.

Population is the sum of births plus in-migration, and it signifies the total market size possible in the area. This is an important metric for economic developers to measure their economic health and investment attraction. Businesses also use this as a metric for market size when evaluating startup, expansion or relocation decisions.

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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For more datasets, click here.

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

About this dataset

Context

The dataset tabulates the Economy 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 Economy. 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 71 (54.20% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Economy population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Economy is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Economy 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 Economy Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Economy by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Economy. The dataset can be utilized to understand the population distribution of Economy by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Economy. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Economy.

Key observations

Largest age group (population): Male # 65-69 years (412) | Female # 60-64 years (490). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Economy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Economy is shown in the following column.
• Population (Female): The female population in the Economy is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Economy for each age group.

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 Economy Population by Gender. You can refer the same here

Socio-Economic Index of 7 variables overlayed to compare with the physical blight index- Education, Median Household Income, Renter Occupied, Single Parent Households, Population Density, Poverty Rate, and Unemployment Rate. This map was used to help question what socio-economic factors correlate with the observance of blighted areas in order to better create strategic decisions on how to best prevent blight.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry, Sex, Ethnicity, Race, and Veteran Status for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB2200NESD01.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by sex, ethnicity, race, and veteran status when classifiable.Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various admini...

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Graph and download economic data for Population Growth for Developing Countries in Europe and Central Asia (SPPOPGROWECA) from 1961 to 2024 about Central Asia, Europe, population, and rate.

United States US: Age Dependency Ratio: % of Working-Age Population: Young data was reported at 28.799 % in 2017. This records a decrease from the previous number of 28.857 % for 2016. United States US: Age Dependency Ratio: % of Working-Age Population: Young data is updated yearly, averaging 33.422 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 51.329 % in 1961 and a record low of 28.799 % in 2017. United States US: Age Dependency Ratio: % of Working-Age Population: Young data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.; ; World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;

Population, Total for United States was 340110988.00000 Persons in January of 2024, according to the United States Federal Reserve. Historically, Population, Total for United States reached a record high of 340110988.00000 in January of 2024 and a record low of 180671000.00000 in January of 1960. Trading Economics provides the current actual value, an historical data chart and related indicators for Population, Total for United States - last updated from the United States Federal Reserve on December of 2025.

Comprehensive socio-economic dataset for Uganda including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Age groups of residents in the City of Mesa.

Comprehensive socio-economic dataset for Armenia including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

BackgroundFederal policy impact analyses in the United States do not incorporate the potential economic benefits of adolescent mental health policies. Understanding the extent to which economic benefits may offset policy costs would support more effective policymaking. This study estimates the relationship between adolescent psychological distress and later health and economic outcomes and uses these estimates to determine the potential economic effects of a hypothetical policy.Methods and findingsThis analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning–enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. The cohort included 3,343 individuals under age 18 years in round 4 who completed the Mental Health Inventory-5 (MHI-5). Round 1 captured 50 explanatory variables that covered domains of potential confounders, including basic demographics, neighborhood environment, family resources, family processes, physical health, school quality, and academic skills. The exposure included a binary variable of clinically significant psychological distress (MHI-5 score of less than or equal to 3) and a categorical variable of symptom severity on the MHI-5. Outcomes covered domains of employment, income, total assets at age 30 years, education, and health approximately 10 years later.Forty-seven percent of the cohort were black and Hispanic, and 4.4% had past-month clinically significant psychological distress. Past-month clinically significant psychological distress in adolescence led to a 6-percentage-point (95% confidence interval [CI] [−0.08, −0.03]) reduction in past-year labor force participation 10 years later and $5,658 (95% CI [−6,772, −4,545]) USD fewer past-year wages earned. We used these results to model the labor market impacts of a hypothetical policy that expanded access to mental health preventive care and reached 10% of youth who would have otherwise developed clinically significant psychological distress. We found that the hypothetical policy could lead to $52 (95% credible interval [51,54]) billion USD in federal budget benefits over 10 years from labor supply impacts alone. This study faced limitations, including potential unmeasured confounding, missing data, and challenges to generalizability.ConclusionsOur findings showed the impacts of adolescent mental health policies on the federal budget and found potentially large effects on the economy if policies achieve population-level change.

United States US: Population: Male: Ages 50-54: % of Male Population data was reported at 6.741 % in 2017. This records a decrease from the previous number of 6.885 % for 2016. United States US: Population: Male: Ages 50-54: % of Male Population data is updated yearly, averaging 5.415 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 7.196 % in 2011 and a record low of 4.426 % in 1986. United States US: Population: Male: Ages 50-54: % of Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Population and Urbanization Statistics. Male population between the ages 50 to 54 as a percentage of the total male population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Comprehensive socio-economic dataset for Russia including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.