Horizon 2020 programme supports access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot (ORDP). To support the validation of scientific results, the pilot focuses on providing access to data needed to validate the scientific results. There are several types of such data, e.g. machine learning data sets, models, measurements, statistical results of experiments, survey outcomes, etc.

This deliverable summarizes the data that are expected to be collected in the course of the project and where and how they are stored. The aspect of providing open access to research data (as required by the European Commission’s Open Research Data Pilot, https://www.openaire.eu/what-is-the-open-research-data-pilot) is addressed in Section 3. Finally, in Section 4 we describe the data sets that were or are expected to be generated within the TRINITY projects and made freely available.

This dataset contains Saudi Arabia Development of the Produced Energy According to Sources 2006-2009 Ministry of Environment, Water and Agriculture Production, Export API data for more datasets to advance energy economics research

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

United States - Sources of Revenue: Investment and Property Income for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms was 3395.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Investment and Property Income for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms reached a record high of 3395.00000 in January of 2021 and a record low of 1093.00000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Investment and Property Income for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms - last updated from the United States Federal Reserve on March of 2025.

This dataset contains Saudi Arabia The Available Power Capacities According to Sources Ministry of Environment, Water and Agriculture Capacity, Export API data for more datasets to advance energy economics research

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Economy, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 33,587, while the mean income for the highest quintile (20% of households with the highest income) is 287,506. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 474,140, which is 164.91% higher compared to the highest quintile, and 1411.68% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 median household income. You can refer the same here

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

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

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

New Zealand NZ: Total R&D Personnel: Per Thousand Labour Force data was reported at 13.373 Per 1000 in 2021. This records a decrease from the previous number of 13.793 Per 1000 for 2019. New Zealand NZ: Total R&D Personnel: Per Thousand Labour Force data is updated yearly, averaging 8.711 Per 1000 from Dec 1989 (Median) to 2021, with 19 observations. The data reached an all-time high of 13.793 Per 1000 in 2019 and a record low of 5.121 Per 1000 in 1991. New Zealand NZ: Total R&D Personnel: Per Thousand Labour Force data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

This dataset contains all the inputs used and output produced from the modified GEOPHIRES for the economic analysis of base case hybrid GDHC system, improved hybrid GDHC system with heat pump and for hot water GDHC. Software required: Microsoft Notepad, Microsoft Excel and GEOPHIRES modified source code

The Socio-Economic Panel (SOEP) is one of the largest and longest-running multidisciplinary household surveys worldwide. Every year, approximately 30,000 people in 15,000 households are interviewed for the SOEP study. The SOEP is also a research-driven infrastructure based at DIW Berlin. The SOEP team prepares survey data for use by researchers around the globe, and team members use the data in research on various topics. Studies based on SOEP data examine diverse aspects of societal change. As part of the Leibniz Association, the SOEP receives funding from the Federal Ministry of Education and Research (BMBF) and from Germany’s state (Länder) governments.

United States - Sources of Revenue: Basic and Applied Research in the Biological and Biomedical Sciences for Scientific Research and Development Services, Establishments Subject to Federal Income Tax Employer Firms was 44078.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Basic and Applied Research in the Biological and Biomedical Sciences for Scientific Research and Development Services, Establishments Subject to Federal Income Tax Employer Firms reached a record high of 44078.00000 in January of 2021 and a record low of 12540.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Basic and Applied Research in the Biological and Biomedical Sciences for Scientific Research and Development Services, Establishments Subject to Federal Income Tax Employer Firms - last updated from the United States Federal Reserve on February of 2025.

About this dataset

Context

The dataset tabulates the population of Economy by race. It includes the population of Economy across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Economy across relevant racial categories.

Key observations

The percent distribution of Economy population by race (across all racial categories recognized by the U.S. Census Bureau): 97.60% are white and 2.40% are multiracial.

https://i.neilsberg.com/ch/economy-in-population-by-race.jpeg" alt="Economy 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 (excluding ethnicity) for the Economy
• Population: The population of the racial category (excluding ethnicity) 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 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

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

The ifo Prussian Economic History Database (iPEHD) is a county-level database covering a rich collection of variables for all counties of Prussia during the 19th century. The Royal Prussian Statistical Office collected these data in a number of censuses over the period 1816-1901, with much county-level information surviving in the archives. These data provide a unique treasure for unprecedented micro-regional empirical research in economic history, analyzing the importance of such factors as education, religion, fertility, and many others for the economic development of Prussia in the 19th century. The service of iPEHD is to provide the data in a digitized and structured way. iPEHD starts with the population census in 1816, which is the first full-scale census released by the Royal Prussian Statistical Office, which had been founded in 1805. The 1816 census covers the 308 Prussian counties at the time. Further extensive census data are available in 1849, 1864, 1871, and 1882, but – as indicated in the following table – many more detailed data were collected in additional years. As the number of counties grew over time, by 1901 the data cover 574 Prussian counties. In total, iPEHD contains more than 1,500 variables and more than half a million data points, all at the county level. These data are drawn from a total of 15 original sources, many of which consist of several volumes.One of the biggest challenges when analyzing historical data is to ensure comparability over time, where the dimension of the units of observation has to be comparable. Our service facilitates the analysis of data at the county level, holding the administrative boundaries fixed. iPEHD stores its data in comma-separated values (csv) format. The raw data are categorized by eight content areas and can be accessed in the raw data section. The codebook section provides information on the names, definitions, labels, and sources for each variable contained in iPEHD.

For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small-scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

Spain ES: Government Researchers: % of National Total data was reported at 14.678 % in 2022. This records a decrease from the previous number of 15.194 % for 2021. Spain ES: Government Researchers: % of National Total data is updated yearly, averaging 17.173 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 20.233 % in 1990 and a record low of 13.232 % in 1985. Spain ES: Government Researchers: % of National Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Spain – Table ES.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

For Spain, beginning in 2008, the R&D questionnaire includes a specific category for on-site consultants undertaking R&D projects in the enterprise; as well as a specific category within the breakdown of current costs.

Since 2004, loans for R&D that are returnable are not included in GBARD, in order to ensure international comparability.

From 2002, R&D expenditure and personnel data for the business enterprise sector include both occasional and regular R&D.

Prior to 1989 R&D personnel data for the Higher Education sector only include researchers. In consequence, total R&D personnel may be underestimated in these years by between 10 and 15 %.

In 1992 there was an upward re-estimation of General University Funds causing a break in series in the financing of HERD and GERD. In 1995, the sources of funds for R&D in the Higher Education sector were reviewed; own funds are now separated from the General University Funds, where they were previously included.

In 1997, the defence objective in GBARD almost doubled in magnitude due to an exceptional contribution by the Ministry for Industry and Energy. The incorporation in 1997 of the Spanish contribution to CERN has involved substantial changes in the “Energy” category.

United States - Sources of Revenue: Original Works of Intellectual Property for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms was 9.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Original Works of Intellectual Property for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms reached a record high of 48.00000 in January of 2019 and a record low of 9.00000 in January of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Original Works of Intellectual Property for Scientific Research and Development Services, Establishments Exempt from Federal Income Tax Employer Firms - last updated from the United States Federal Reserve on March of 2025.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Grant township, Grand Traverse County, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 22,447, while the mean income for the highest quintile (20% of households with the highest income) is 203,020. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 313,194, which is 154.27% higher compared to the highest quintile, and 1395.26% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Grant township median household income. You can refer the same here

Argentina AR: Space Programmes: % of Civil GBARD data was reported at 7.871 % in 2012. This records a decrease from the previous number of 8.076 % for 2011. Argentina AR: Space Programmes: % of Civil GBARD data is updated yearly, averaging 4.195 % from Dec 1996 (Median) to 2012, with 17 observations. The data reached an all-time high of 8.076 % in 2011 and a record low of 3.115 % in 2000. Argentina AR: Space Programmes: % of Civil GBARD data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.MSTI: Government Budgets for Research and Development: Non OECD Member: Annual.

In Argentina, the coverage of the business enterprises was expanded in 2015. BERD data are derived from a new survey from 2009. Since 1997, data for human resources relate to R&D. Before that, human resources data were expressed in terms of Science and Technology Activities (STA), involving R&D and diffusion activities of S&T (library services, training services, conferences, etc.). These have not been transferred to the OECD database. Since 2002, the source of funds data for private non-profit organisations, universities and S&T public organisations are requested for R&D. Before 2002, these sources of funds data were requested in terms of STA. These data were converted into R&D by means of a coefficient for each sector of performance. The main source of funds for science and technology activities in Argentina is the National Budget.

About this dataset

Context

The dataset tabulates the Economy 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 Economy. The dataset can be utilized to understand the population distribution of Economy by age. For example, using this dataset, we can identify the largest age group in Economy.

Key observations

The largest age group in Economy, PA was for the group of age 60-64 years with a population of 883 (9.75%), according to the 2021 American Community Survey. At the same time, the smallest age group in Economy, PA was the 80-84 years with a population of 232 (2.56%). 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 Economy is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Economy 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 Economy Population by Age. You can refer the same here