Migration flows are derived from the relationship between the location of current residence in the American Community Survey (ACS) sample and the responses given to the migration question "Where did you live 1 year ago?". There are flow statistics (moved in, moved out, and net moved) between county or minor civil division (MCD) of residence and county, MCD, or world region of residence 1 year ago. Estimates for MCDs are only available for the 12 strong-MCD states, where the MCDs have the same government functions as incorporated places. Migration flows between metropolitan statistical areas are available starting with the 2009-2013 5-year ACS dataset. Flow statistics are available by three or four variables for each dataset starting with the 2006-2010 5-year ACS datasets. The variables change for each dataset and do not repeat in overlapping datasets. In addition to the flow estimates, there are supplemental statistics files that contain migration/geographical mobility estimates (e.g. nonmovers, moved to a different state, moved from abroad) for each county, MCD, or metro area.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1930 households: This dataset includes all households from the 1930 US census.

IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

Section 2

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1930 census data was collected in April 1930. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edite

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2017 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1910 census data was collected in April 1910. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Section 2

This dataset was created on 2020-01-10 23:47:27.924 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1910 households: The Integrated Public Use Microdata Series (IPUMS) Complete Count Data are historic individual and household census records and are a unique source for research on social and economic change.

IPUMS 1910 persons: This dataset includes all individuals from the 1910 US census.

This study was conducted under the auspices of the Center for Studies in Demography and Ecology at the University of Washington. It is a nationally representative sample of the population of the United States in 1900, drawn from the manuscript returns of individuals enumerated in the 1900 United States Census. Household variables include region, state and county of household, size of household, and type and ownership of dwelling. Individual variables for each household member include relationship to head of household, race, sex, age, marital status, number of children, and birthplace. Immigration variables include parents' birthplace, year of immigration and number of years in the United States. Occupation variables include occupation, coded by both the 1900 and 1950 systems, and number of months unemployed. Education variables include number of months in school, whether respondents could read or write a language, and whether they spoke English. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07825.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2020 // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on this file. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. A time series of estimates is produced, beginning with the census. The reference date for all estimates is the first of the month. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

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

The dataset, provided both in comma-separated values (.csv) and the more informative Stata (.dta) format, contains place/year demographic data on more than 300 rural Alaska communities annually for 1990 to 2022 -- about 10,000 place/years. For each of the available place/years, the data include population estimates from the Alaska Department of Labor and Workforce Development or (in Census years) from the US Census. For a subset consisting of 104 northern or western Alaska (Arctic/subarctic) towns and villages, the dataset also contains yearly estimates of natural increase (births minus deaths) and net migration (population minus last year's population plus natural increase). Natural increase was calculated from birth and death counts provided confidentially to researchers by the Alaska Health Analytics and Vital Records Section (HAVRS). By agreement with HAVRS, the community-level birth and death counts are not available for publication. Population, natural increase, and net migration estimates reflect mid-year values, or change over the past fiscal rather than calendar year. For example, the natural increase value for a community in 2020 is based on births and deaths of residents from July 1, 2019 to June 31, 2020. We emphasize that all values here are best estimates, based on records of the Alaska government organizations. The dataset contains 19 variables: placename Place name (string) placenum Place name (numeric) placefips Place FIPS code year Year borough Borough name boroughfips Borough FIPS code latitude Latitude (decimal, - denotes S) longitude Longitude (decimal, - denotes W) town Village {0:pop2020<2,000} or town {1:pop2020>2,000} village104 104 selected Arctic/rural communities {0,1} arctic43 43 Arctic communities {0,1}, Hamilton et al. 2016 north37 37 Northern Alaska communities {0,1), Hamilton et al. 2016 pop Population (2022 data) cpopP Change in population, percent natinc Natural increase: births-deaths natincP Natural increase, percent netmig Net migration estimate netmigP Net migration, percent nipop Population without migration Three of these variables flag particular subsets of communities. The first two subsets (43 or 37 places) were analyzed in earlier publications, so the flags might be useful for replications or comparisons. The third subset (104 places) is a newer, expanded group of Arctic/subarctic towns and villages for which natural increase and net migration estimates are now available. The flag variables are: If arctic43 = 1 Subset consisting of 43 Arctic towns and villages, previously studied in three published articles: 1. Hamilton, L.C. & A.M. Mitiguy. 2009. “Visualizing population dynamics of Alaska’s Arctic communities.” Arctic 62(4):393–398. https://doi.org/10.14430/arctic170 2. Hamilton, L.C., D.M. White, R.B. Lammers & G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. https://doi.org/10.1007/s11111-011-0145-1 3. Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers & H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. https://doi.org/10.1007/s11111-016-0259-6 If north37 = 1 Subset consisting of 37 northern Alaska towns and villages, previously analyzed for comparison with Nunavut and Greenland in a paper on demographics of the Inuit Arctic: 4. Hamilton, L.C., J. Wirsing & K. Saito. 2018. “Demographic variation and change in the Inuit Arctic.” Environmental Research Letters 13:11507. https://doi.org/10.1088/1748-9326/aae7ef If village104 = 1 Expanded group consisting of 104 communities, including all those in the arctic43 and north37 subsets. This group includes most rural Arctic/subarctic communities that had reasonably complete, continuous data, and 2018 populations of at least 100 people. These data were developed by updating older work and drawing in 61 additional towns or villages, as part of the NSF-supported Arctic Village Dynamics project (OPP-1822424).

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2016 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

The purpose of this data set is to allow exploration between various types of data that is commonly collected by the US government across the states and the USA as a whole. The data set consists of three different types of data:

• Census and Geographic Data;
• Energy Data; and
• Economic Data.

When creating the data set, I combined data from many different types of sources, all of which are cited below. I have also provided the fields included in the data set and what they represent below. I have not performed any research on the data yet, but am going to dive in soon. I am particularly interested in the relationships between various types of data (i.e. GDP or birth rate) in prediction algorithms. Given that I have compiled 5 years’ worth of data, this data set was primarily constructed with predictive algorithms in mind.

An additional note before you delve into the fields: * There could have been many more variables added across many different fields of metrics. I have stopped here, but it could potentially be beneficial to observe the interaction of these variables with others (i.e. the GDP of certain industries, the average age in a state, the male/female gender ratio, etc.) to attempt to find additional trends.

Census and Geographic Data

• StateCodes: The state 2-letter abbreviations. Note that I added "US" for the United States.
• Region: The number corresponding to the region the state lies within, according to the 2010 census. (1 = Northeast, 2 = Midwest, 3 = South, 4 = West)
• Division: The number corresponding to the division the state lies within, according to the 2010 census. (1 = New England, 2 = Middle Atlantic, 3 = East North Central, 4 = West North Central, 5 = South Atlantic, 6 = East South Central, 7 = West South Central, 8 = Mountain, 9 = Pacific)
• Coast: Whether the state shares a border with an ocean. (1 = Yes, 0 = No)
• Great Lakes: Whether the state shares a border with a great lake. (1 = Yes, 0 = No
• CENSUS2010POP: 4/1/2010 resident total Census 2010 population
• POPESTIMATE{year}: 7/1/{year} resident total population estimate
• RBIRTH{year}: Birth rate in period 7/1/{year - 1} to 6/30/
• RDEATH{year}: Death rate in period 7/1/{year - 1} to 6/30/
• RNATURALINC{year}: Natural increase rate in period 7/1/{year - 1} to 6/30/
• RINTERNATIONALMIG{year}: Net international migration rate in period 7/1/{year - 1} to 6/30/
• RDOMESTICMIG{year}: Net domestic migration rate in period 7/1/{year - 1} to 6/30/
• RNETMIG{year}: Net migration rate in period 7/1/{year - 1} to 6/30/

As noted from the census:

Net international migration for the United States includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. Net international migration for Puerto Rico includes the migration of native and foreign-born populations between the United States and Puerto Rico.

Codes for most of the data, information about the geographic terms and coditions, and more information about the methodology behind the population estimates can be found on the US Census website.

Energy Data

• TotalC{year}: Total energy consumption in billion BTU in given year.
• TotalP{year}: Total energy production in billion BTU in given year.
• TotalE{year}: Total Energy expenditures in million USD in given year.
• TotalPrice{year}: Total energy average price in USD/million BTU in given year.
• TotalC{first year}–{second year}: The first year’s total energy consumption divided by the second year’s total energy consumption, times 100. (The percent change between years in total energy consumption.)
• TotalP{first year}–{second year}: The first year’s total energy production divided by the second year’s total energy production, times 100. (The percent change between years in total energy production.)
• TotalE{first year}–{second year}: The first year’s total energy expenditure divided by the second year’s total energy expenditure, times 100. (The percent change between years in total energy expenditure.)
• TotalPrice{first year}–{second year}: The first year’s total energy average price divided by the second year’s total energy average price, times 100. (The percent change between years in total energy average price.)
• BiomassC{year}: Biomass total consumption in billion BTU in given year.
• CoalC{year}: Coal total consumption in billion BTU in given year.
• CoalP{year}: Coal total production in billion BTU in given year.
• CoalE{year}: Coal total expenditures in million USD in given year.
• CoalPrice{year}:...

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facilitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems. Detailed metadata will be found in ipumsi_6.3_us_1960_ddic.html within the Data Package. The related metadata describes the content of the extraction of the specified sample from the IPUMS International on-line extraction system.

About this dataset

Context

The dataset tabulates the Connecticut 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 Connecticut. 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 2.21 million (61.52% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

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

Variables / Data Columns

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

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facilitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems. Detailed metadata will be found in ipumsi_6.3_us_2000_ddic.html within the Data Package. The related metadata describes the content of the extraction of the specified sample from the IPUMS International on-line extraction system.

About this dataset

Context

The dataset tabulates the District of Columbia 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 District of Columbia. 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 460,903 (68.58% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

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

Variables / Data Columns

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

Abstract

This dataset includes all individuals from the 1870 US census.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.

The official enumeration day of the 1870 census was 1 June 1870. The main goal of an early census like the 1870 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.

Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT