https://www.icpsr.umich.edu/web/ICPSR/studies/35605/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/35605/terms
The United States Census Bureau has conducted surveys of manufacturing activity since 1810 with fluctuating frequency. Between 1919 and 1939 the Census of Manufactures (CM) was conducted biennially. This data collection consists of individual-plant data from the Census of Manufactures for 1929, 1931, 1933, and 1935, the only years in this span for which original returns are available. The records of the Cotton Goods Industry have been coded to produce an electronic dataset to provide the basis for microeconomic evidence for the study of the Great Depression. The dataset contains observations on: basic information about the plants (e.g. name, location, owner, etc.), products made and materials used, operation and working hours, employment, wages and salaries, costs and amount of materials used, value of products and processing tax (1933 and 1935), machinery, and power used.
Historical population as enumerated and corrected from 1790 through 2020. North Carolina was one of the 13 original States and by the time of the 1790 census had essentially its current boundaries. The Census is mandated by the United States Constitution and was first completed for 1790. The population has been counted every ten years hence, with some limitations. In 1790 census coverage included most of the State, except for areas in the west, parts of which were not enumerated until 1840. The population for 1810 includes Walton County, enumerated as part of Georgia although actually within North Carolina. Historical populations shown here reflect the population of the respective named county and not necessarily the population of the area of the county as it was defined for a particular census. County boundaries shown in maps reflect boundaries as defined in 2020. Historic boundaries for some counties may include additional geographic areas or may be smaller than the current geographic boundaries. Notes below list the county or counties with which the population of a currently defined county were enumerated historically (Current County: Population counted in). The current 100 counties have been in place since the 1920 Census, although some modifications to the county boundaries have occurred since that time. For historical county boundaries see: Atlas of Historical County Boundaries Project (newberry.org)County Notes: Note 1: Total for 1810 includes population (1,026) of Walton County, reported as a Georgia county but later determined to be situated in western North Carolina. Total for 1890 includes 2 Indians in prison, not reported by county. Note 2: Alexander: *Iredell, Burke, Wilkes. Note 3: Avery: *Caldwell, Mitchell, Watauga. Note 4: Buncombe: *Burke, Rutherford; see also note 22. Note 5: Caldwell: *Burke, Wilkes, Yancey. Note 6: Cleveland: *Rutherford, Lincoln. Note 7: Columbus: *Bladen, Brunswick. Note 8: Dare: *Tyrrell, Currituck, Hyde. Note 9: Hoke: *Cumberland, Robeson. Note 10: Jackson: *Macon, Haywood. Note 11: Lee: *Moore, Chatham. Note 12: Lenoir: *Dobbs (Greene); Craven. Note 13: McDowell: *Burke, Rutherford. Note 14: Madison: *Buncombe, Yancey. Note 15: Mitchell: *Yancey, Watauga. Note 16: Pamlico: *Craven, Beaufort. Note 17: Polk: *Rutherford, Henderson. Note 18: Swain: *Jackson, Macon. Note 19: Transylvania: *Henderson, Jackson. Note 20: Union: *Mecklenburg, Anson. Note 21: Vance: *Granville, Warren, Franklin. Note 22: Walton: Created in 1803 as a Georgia county and reported in 1810 as part of Georgia; abolished after a review of the State boundary determined that its area was located in North Carolina. By 1820 it was part of Buncombe County. Note 23: Watauga: *Ashe, Yancey, Wilkes; Burke. Note 24: Wilson: *Edgecombe, Nash, Wayne, Johnston. Note 25: Yancey: *Burke, Buncombe. Note 26: Alleghany: *Ashe. Note 27: Haywood: *Buncombe. Note 28: Henderson: *Buncombe. Note 29: Person: Caswell. Note 30: Clay: Cherokee. Note 31: Graham: Cherokee. Note 32: Harnett: Cumberland. Note 33: Macon: Haywood.
Note 34: Catawba: Lincoln. Note 35: Gaston: Lincoln. Note 36: Cabarrus: Mecklenburg.
Note 37: Stanly: Montgomery. Note 38: Pender: New Hanover. Note 39: Alamance: Orange.
Note 40: Durham: Orange, Wake. Note 41: Scotland: Richmond. Note 42: Davidson: Rowan. Note 43: Davie: Rowan.Note 44: Forsyth: Stokes. Note 45: Yadkin: Surry.
Note 46: Washington: Tyrrell.Note 47: Ashe: Wilkes. Part III. Population of Counties, Earliest Census to 1990The 1840 population of Person County, NC should be 9,790. The 1840 population of Perquimans County, NC should be 7,346.
There were almost 700 thousand slaves in the US in 1790, which equated to approximately 18 percent of the total population, or roughly one in every six people. By 1860, the final census taken before the American Civil War, there were four million slaves in the South, compared with less than 0.5 million free African Americans in all of the US. Of the 4.4 million African Americans in the US before the war, almost four million of these people were held as slaves; meaning that for all African Americans living in the US in 1860, there was an 89 percent* chance that they lived in slavery. A brief history Trans-Atlantic slavery began in the early sixteenth century, when the Portuguese and Spanish forcefully brought captured African slaves to the New World, in order to work for them. The British Empire introduced slavery to North America on a large scale, and the economy of the British colonies there depended on slave labor, particularly regarding cotton, sugar and tobacco output. In the seventeenth and eighteenth century the number of slaves being brought to the Americas increased exponentially, and at the time of American independence it was legal in all thirteen colonies. Although slavery became increasingly prohibited in the north, the number of slaves remained high during this time as they were simply relocated or sold from the north to the south. It is also important to remember that the children of slaves were also viewed as property, and (apart from some very rare cases) were born into a life of slavery. Abolition and the American Civil War In the years that followed independence, the Northern States began gradually prohibiting slavery, and it was officially abolished there by 1805, and the importation of slave labor was prohibited nationwide from 1808 (although both still existed in practice after this). Business owners in the Southern States however depended on slave labor in order to meet the demand of their rapidly expanding industries, and the issue of slavery continued to polarize American society in the decades to come. This culminated in the election of President Abraham Lincoln in 1860, who promised to prohibit slavery in the newly acquired territories to the west, leading to the American Civil War from 1861 to 1865. Although the Confederacy (south) were victorious in much of the early stages of the war, the strength in numbers of the northern states (including many free, black men), eventually resulted in a victory for the Union (north), and the nationwide abolishment of slavery with the Thirteenth Amendment in 1865. Legacy In total, an estimated twelve to thirteen million Africans were transported to the Americas as slaves, and this does not include the high number who did not survive the journey (which was as high as 23 percent in some years). In the 150 years since the abolishment of slavery in the US, the African-American community have continuously campaigned for equal rights and opportunities that were not afforded to them along with freedom. The most prominent themes have been the Civil Rights Movement, voter suppression, mass incarceration and the relationship between the police and the African-American community has taken the spotlight in recent years.
https://www.icpsr.umich.edu/web/ICPSR/studies/2863/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/2863/terms
The objective of this data collection was to examine inequalities of wealth and the geographic distribution of wealthy individuals in late 18th- and early 19th-century New York and to investigate wealth in relationship to occupation and location. For this study, the entire set of tax assessment records and United States Census records for New York City were computerized and occupational status was added for all entries. The collection addresses topics such as social class structure, demographic factors, occupational status and geographic distribution, property values and geographic distribution, and the relationship of these factors to the political system. Units of analysis were individual property owners and renters for the tax assessment data and heads of households for the census data. Data collected included the individual's name, address, occupation, sex, and race, the type, quantity, and value of real and personal property, and the type and occupancy of the structure at the address. Occupational data from city directories were used to supplement the tax and census data.
Table from the American Community Survey (ACS) S1810 disability characteristics by age. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): S1810Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):<a href='https://www.census.gov/programs-surveys/acs/about.html' style='color:rgb(0, 121, 193); text-decoration-line:none; fon
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Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..For cognitive difficulty, ambulatory difficulty, and self-care difficulty, the 'Population under 18 years' includes persons aged 5 to 17. Children under 5 are not included in these measures..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.
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Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..For cognitive difficulty, ambulatory difficulty, and self-care difficulty, the 'Population under 18 years' includes persons aged 5 to 17. Children under 5 are not included in these measures..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.
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Context
The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Santa Barbara: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Santa Barbara median household income by age. You can refer the same here
This polygon shapefile contains county boundaries for the United States in 1810. Attributes include county and state names as well as FIPS identification numbers and county area estimates. Territories enumerated by the U.S. Census are also included. This layer is part of a collection of historical United States county boundary files (HUSCO), from each decade ranging from 1790-1999.
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Context
The dataset tabulates the population of Westfield by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Westfield. The dataset can be utilized to understand the population distribution of Westfield by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Westfield. 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 Westfield.
Key observations
Largest age group (population): Male # 15-19 years (2,063) | Female # 15-19 years (1,810). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Westfield Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Context
The dataset tabulates the Lincoln 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 Lincoln. The dataset can be utilized to understand the population distribution of Lincoln by age. For example, using this dataset, we can identify the largest age group in Lincoln.
Key observations
The largest age group in Lincoln, CA was for the group of age 5-9 years with a population of 3,828 (7.78%), according to the 2021 American Community Survey. At the same time, the smallest age group in Lincoln, CA was the 85+ years with a population of 1,810 (3.68%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Lincoln Population by Age. You can refer the same here
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Context
The dataset tabulates the population of Grand Blanc charter township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Grand Blanc charter township. The dataset can be utilized to understand the population distribution of Grand Blanc charter township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Grand Blanc charter township. 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 Grand Blanc charter township.
Key observations
Largest age group (population): Male # 10-14 years (1,949) | Female # 55-59 years (1,810). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Grand Blanc charter township Population by Gender. You can refer the same here
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Cheltenham township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Cheltenham township. The dataset can be utilized to understand the population distribution of Cheltenham township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Cheltenham township. 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 Cheltenham township.
Key observations
Largest age group (population): Male # 10-14 years (1,609) | Female # 20-24 years (1,810). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Cheltenham township Population by Gender. You can refer the same here
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Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..For cognitive difficulty, ambulatory difficulty, and self-care difficulty, the 'Population under 18 years' includes persons aged 5 to 17. Children under 5 are not included in these measures..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.
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Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..For cognitive difficulty, ambulatory difficulty, and self-care difficulty, the 'Population under 18 years' includes persons aged 5 to 17. Children under 5 are not included in these measures..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.
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Context
The dataset tabulates the Summit township 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 Summit township. The dataset can be utilized to understand the population distribution of Summit township by age. For example, using this dataset, we can identify the largest age group in Summit township.
Key observations
The largest age group in Summit township, Jackson County, Michigan was for the group of age 55-59 years with a population of 1,810 (7.86%), according to the 2021 American Community Survey. At the same time, the smallest age group in Summit township, Jackson County, Michigan was the 80-84 years with a population of 581 (2.52%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Summit township Population by Age. You can refer the same here
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2014-2018 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the .Evaluation Report Covering Disability....While the 2014-2018 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....
Disabled Population by Age by City. Please note that # refer to the number of people within the age group with a disability and % refers to the percent of those with a disability within that age group. Source: U.S. Census Bureau; 2012-2016 American Community Survey 5-Year Estimates, Table S1810.
Disability by age: Please note that number refers to the number of people within the age group with a disability and percent refers to the percent of those with a disability within that age group. Source: U.S. Census Bureau; 2011-2015 American Community Survey 5-Year Estimates, Table S1810.
https://www.icpsr.umich.edu/web/ICPSR/studies/35605/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/35605/terms
The United States Census Bureau has conducted surveys of manufacturing activity since 1810 with fluctuating frequency. Between 1919 and 1939 the Census of Manufactures (CM) was conducted biennially. This data collection consists of individual-plant data from the Census of Manufactures for 1929, 1931, 1933, and 1935, the only years in this span for which original returns are available. The records of the Cotton Goods Industry have been coded to produce an electronic dataset to provide the basis for microeconomic evidence for the study of the Great Depression. The dataset contains observations on: basic information about the plants (e.g. name, location, owner, etc.), products made and materials used, operation and working hours, employment, wages and salaries, costs and amount of materials used, value of products and processing tax (1933 and 1935), machinery, and power used.