Projected Population by Single Year of Age, Sex, Race, Hispanic Origin, and Nativity for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: 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.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This data package includes the underlying data to replicate the charts and calculations presented in As US population growth slows, we need to reset expectations for economic data, PIIE Policy Brief 25-5.

If you use the data, please cite as:

Kolko, Jed. 2025. As US population growth slows, we need to reset expectations for economic data. PIIE Policy Brief 25-5. Washington: Peterson Institute for International Economics.

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change; for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through March. // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. // Note: 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. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in September 2018. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) 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 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.

Context

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050.

Content

The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

Fork this kernel to get started.

Acknowledgements

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

https://cloud.google.com/bigquery/public-data/international-census

Dataset Source: www.census.gov

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 Steve Richey from Unsplash.

Inspiration

What countries have the longest life expectancy?

Which countries have the largest proportion of their population under 25?

Which countries are seeing the largest net migration?

Isolation caused by anthropogenic habitat fragmentation can destabilize populations. Populations relying on the inflow of immigrants can face reduced fitness due to inbreeding depression as fewer new individuals arrive. Empirical studies of the demographic consequences of isolation are critical to understanding how populations persist through changing conditions. We used a 34-year demographic and environmental dataset from a population of cooperatively-breeding Florida Scrub-Jays (Aphelocoma coerulescens) to create mechanistic models linking environmental and demographic factors to population growth rates. We found that the population has not declined despite both declining immigration and increasing inbreeding, owing to a coinciding response in breeder survival. We find evidence of density-dependent immigration, breeder survival, and fecundity, indicating that interactions between vital rates and local density play a role in buffering the population against change. Our study elucidates..., All work was approved by the Cornell University Institutional Animal Care and Use Committee (IACUC 2010-0015) and authorized by permits from the US Fish and Wildlife Service (TE824723-8), the US Geological Survey (banding permit 07732), and the Florida Fish and Wildlife Conservation Commission (LSSC-10-00205)., , # Density dependence maintains long-term stability despite increased isolation and inbreeding in the Florida Scrub-Jay

https://doi.org/10.5061/dryad.p2ngf1vz3

This dataset contains raw census data (FullLOI.txt), derived vital rates (vr_clean_F_4stageDemo.rdata, vr_clean_M_4stageDemo.rdata), ecological metrics (reqsoi_update.txt, acorns_update.txt, TerrYrBurnArea.txt, TerrMap.txt, TerrsToKeep.txt, densityCalcDemo.rdata, env_var_updateDemo.txt, envFac_annual.txt), pedigree information (pedInbr.txt, kinship_coef_Demo.rdata), and demographic models created using these data (vr_modelsDemo_revision_20240518.rdata, vr_modelsDemo.rdata, Demo_LTRE_results_20240518.rdata), including model validation results (vr_modelsDemo_validation_revisions_20240518.rdata).

Description of the data and file structure

FullLOI.txt

• USFWS: Individual ID
• Year: Year of census
• TerrYr: Te...

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).

Abstract (en): This dataset provides annual population projections for the 50 states and the District of Columbia by age, sex, and race for the years 1986 through 2010. The projections were made using a mathematical projection model called the cohort-component method. This method allows separate assumptions to be made for each of the components of population change: births, deaths, internal migration, and international migration. The projections are consistent with the July 1, 1986 population estimates for states. In general, the projections assume a slight increase in the national levels of fertility, an increasing level of life expectancy, and a decreasing level of net international migration. Internal migration assumptions are based on the annual state-to-state migration data for the years 1975-1986. The resident population of the 50 states and the District of Columbia. Data are included for the years 1986 and 1987 as well as 1988-2010. Figures for 1986 are estimates, while those for 1987 and succeeding years are projections.

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for States and Counties // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration in 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. // 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. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureaus 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., V2014) 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: http://www.census.gov/popest/index.html.

Projected Population by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: 2012 to 2060 File: 2012 National Population Projections Source: U.S. Census Bureau, Population Division Release Date: December 2012 NOTE: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. The projections generally do not precisely agree with population estimates available elsewhere on the Census Bureau website for methodological reasons. Where both estimates and projections are available for a given time reference, we recommend that you use the population estimates as the measure of the current population. For detailed information about the methods used to create the population projections, see http://www.census.gov/population/projections/methodology/. *** The U.S. Census Bureau periodically produces projections of the United States resident population by age, sex, race, and Hispanic origin. Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. In some cases, several series of projections are produced based on alternative assumptions for future fertility, life expectancy, net international migration, and (for state-level projections) state-to-state or domestic migration. Additional information is available on the Population Projections website: http://www.census.gov/population/projections/.

Resident population of New York State and counties produced by the U.S. Census Bureau. Estimates are based on decennial census counts (base population), intercensal estimates, postcensal estimates and administrative records. Updates are made annually using current data on births, deaths, and migration to estimate population change. Each year beginning with the most recent decennial census the series is revised, these new series of estimates are called vintages.

Untangling the spatial and temporal processes that influence population dynamics of migratory species is challenging, because changes in abundance are shaped by variation in vital rates across heterogeneous habitats and throughout the annual cycle. We developed a full-annual-cycle, integrated population model and used demographic data collected between 2011 and 2014 in southern Indiana and Belize to estimate stage-specific vital rates of a declining migratory songbird, the Wood Thrush (Hylocichla mustelina). Our primary objective was to understand how spatial and temporal variation in demography contributes to local and regional population growth. Our full-annual-cycle model allowed us to estimate: 1) age-specific, seasonal survival probabilities, including latent survival during both spring and autumn migration, and 2) how the relative contribution of vital rates to population growth differed among habitats. Wood Thrushes in our study populations experienced the lowest apparent survival rates during migration and apparent survival was lower during spring migration than during fall migration. Both mortality and high dispersal likely contributed to low apparent survival during spring migration. Population growth in high-quality habitat was most sensitive to variation in fecundity and apparent survival of juveniles during spring migration, whereas population growth in low-quality sites was most sensitive to adult apparent breeding-season survival. These results elucidate how full-annual-cycle vital rates, particularly apparent survival during migration, interact with spatial variation in habitat quality to influence population dynamics in migratory species.

Annual Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for the United States, States, and Puerto Rico // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration (except for Puerto Rico) 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. // 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. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureaus 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., V2014) 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: http://www.census.gov/popest/index.html.

The data collection contains population projections for UK ethnic groups and all local area by age (single year of age up to 100+) and sex. Included in the data set are also input data to the cohort component model that was used to project populations into the future-fertility rates, mortality rates, international migration flows and internal migration probabilities. Also included in data set are output data: Number of deaths, births and internal migrants. All data included are for the years 2011 to 2061. We have produced two ethnic population projections for UK local authorities, based on information on 2011 Census ethnic populations and 2010-2011-2012 ethnic components. Both projections align fertility and mortality assumptions to ONS assumptions. Where they differ is in the migration assumptions. In LEEDS L1 we employ internal migration rates for 2001 to 2011, including periods of boom and bust. We use a new assumption about international migration anticipating that the UK may leave the EU (BREXIT). In LEEDS L2 we use average internal migration rates for the 5 year period 2006-11 and the official international migration flow assumptions with a long term balance of +185 thousand per annum. This project aims to understand and to forecast the ethnic transition in the United Kingdom's population at national and sub-national levels. The ethnic transition is the change in population composition from one dominated by the White British to much greater diversity. In the decade 2001-2011 the UK population grew strongly as a result of high immigration, increased fertility and reduced mortality. Both the Office for National Statistics (ONS) and Leeds University estimated the growth or decline in the sixteen ethnic groups making up the UK's population in 2001. The 2011 Census results revealed that both teams had over-estimated the growth of the White British population and under-estimated the growth of the ethnic minority populations. The wide variation between our local authority projected populations in 2011 and the Census suggested inaccurate forecasting of internal migration. We propose to develop, working closely with ONS as our first external partner, fresh estimates of mid-year ethnic populations and their components of change using new data on the later years of the decade and new methods to ensure the estimates agree in 2011 with the Census. This will involve using population accounting theory and an adjustment technique known as iterative proportional fitting to generate a fully consistent set of ethnic population estimates between 2001 and 2011. We will study, at national and local scales, the development of demographic rates for ethnic group populations (fertility, mortality, internal migration and international migration). The ten year time series of component summary indicators and age-specific rates will provide a basis for modelling future assumptions for projections. We will, in our main projection, align the assumptions to the ONS 2012-based principal projection. The national assumptions will need conversion to ethnic groups and to local scale. The ten years of revised ethnic-specific component rates will enable us to study the relationships between national and local demographic trends. In addition, we will analyse a consistent time series of local authority internal migration. We cannot be sure, at this stage, how the national-local relationships for each ethnic group will be modelled but we will be able to test our models using the time series. Of course, all future projections of the population are uncertain. We will therefore work to measure the uncertainty of component rates. The error distributions can be used to construct probability distributions of future populations via stochastic projections so that we can define confidence intervals around our projections. Users of projections are always interested in the impact of the component assumptions on future populations. We will run a set of reference projections to estimate the magnitude and direction of impact of international migrations assumptions (net effect of immigration less emigration), of internal migration assumptions (the net effect of in-migration less out-migration), of fertility assumptions compared with replacement level, of mortality assumptions compared with no change and finally the effect of the initial age distribution (i.e. demographic potential). The outputs from the project will be a set of technical reports on each aspect of the research, journal papers submitted for peer review and a database of projection inputs and outputs available to users via the web. The demographic inputs will be subject to quality assurance by Edge Analytics, our second external partner. They will also help in disseminating these inputs to local government users who want to use them in their own ethnic projections. In sum, the project will show how a wide range of secondary data sources can be used in theoretically refined demographic models to provide us with a more reliable picture of how the UK population is going to change in ethnic composition. Base year data (2011) are derived from the 2011 census, vital statistics and ONS migration data. Subsequent population data are computed with a cohort component model.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

Abstract (en): Detailed demographic characteristics of the population of the United States from 1870 to 1960 are contained in this data collection. Included are state-level estimates of the nation's inhabitants by sex, race, nativity and age, as well as intercensal migration calculated by age, race, and sex. The basic information recorded in this collection was obtained from the decennial censuses of the United States or estimated by the principal investigators from material collected by the decennial censuses. The collection is comprised of thirteen separate data files. Each contains information for every state in the nation. All parts have a rectangular file structure with one record per case, with the number of cases ranging from 50 to 2,891, and the record length from 203 to 2,930 per part. Standard geographic identifying codes used in all of the files permit the combination of two or more of the files as research interests dictate. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. 2011-08-31 SAS, SPSS, and Stata setups have been added to this data collection.2006-01-12 All files were removed from dataset 14 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 14 and flagged as study-level files, so that they will accompany all downloads. This collection was made available to ICPSR through the Population Studies Center of the University of Pennsylvania.

Annual resident population estimates, estimated components of resident population change, and rates of the components of resident population change for states and counties: April 1, 2010 to July 1, 2015.

From http://www.census.gov/popest/data/counties/totals/2015/CO-EST2015-alldata.html

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth, PIIE Working Paper 19-3.

If you use the data, please cite as: Huertas, Gonzalo, and Jacob Funk Kirkegaard. (2019). The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth. PIIE Working Paper 19-3. Peterson Institute for International Economics.

Infectious diseases can cause steep declines in wildlife populations, leading to changes in genetic diversity that may affect the susceptibility of individuals to infection and the overall resilience of populations to pathogen outbreaks. Here, we examine evidence for a genetic bottleneck in a population of American crows (Corvus brachyrhynchos) before and after the emergence of West Nile virus (WNV). More than 50% of marked birds in this population were lost over the two-year period of the epizootic, representing a 10-fold increase in adult mortality. Using analyses of SNPs and microsatellite markers, we tested for evidence of a genetic bottleneck and compared levels of inbreeding and immigration in the pre- and post-WNV populations. Counter to expectations, genetic diversity (allelic diversity and the number of new alleles) increased after WNV emergence. This was likely due to increases in immigration, as the estimated membership coefficients were lower in the post-WNV population. Simultaneously, however, the frequency of inbreeding appeared to increase: mean inbreeding coefficients were higher among SNP markers, and heterozygosity-heterozygosity correlations were stronger among microsatellite markers, in the post-WNV population. These results indicate that loss of genetic diversity at the population level is not an inevitable consequence of a population decline, particularly in the presence of gene flow. The changes observed in post-WNV crows could have very different implications for their response to future pathogen risks, potentially making the population as a whole more resilient to a changing pathogen community, while increasing the frequency of inbred individuals with elevated susceptibility to disease. Methods Study population and data collection. Crows in the Ithaca, New York, population are cooperative breeders. They live in groups of up to 14 birds, including a socially bonded pair of adults as well as 0-12 auxiliary birds, which are usually offspring from previous broods). Although auxiliaries usually do not contribute offspring to the brood, molecular work in the post-WNV population indicates that auxiliary males occasionally do sire extra-pair offspring with the female breeder, arising both through incest (mothers mating with their adult auxiliary sons) and through matings between non-relatives (e.g., unrelated step-mothers and adult auxiliary males). Genetic samples were collected from crow nestlings from 1990–2011. We collected blood (~150 ul) from the brachial vein of nestlings and banded them with unique combinations of metal bands, color bands, and patagial tags on days 24–30 after hatching. DNA was extracted from samples using DNeasy tissue kits (Qiagen, Valencia, CA) following the manufacturer’s protocol. All fieldwork with American crows was carried out under protocols approved by the Institutional Animal Care and Use Committees of Binghamton University (no. 537-03 and 607-07) and Cornell University (no. 1988–0210). The pre-WNV dataset included samples collected between 1990 and 2002. The 2002 nestlings were sampled prior to WNV emergence, as nestlings fledge the nest between May and July, whereas WNV mortality typically occurs between August and October in this crow population. The post-WNV samples were collected between 2005 and 2011. Samples collected immediately after WNV emergence (2003 and 2004) were not included in the analysis to allow time for the birds to respond to the population loss. We maximized independence of the birds selected for analysis by including only one randomly chosen offspring per brood and no more than two broods per family group in the pre-and post-WNV samples, with each brood per family group separated by the maximum number of years possible within the pre- or post-WNV sampling periods (1990–2002 pre-WNV; 2005–2011 post-WNV; Figure S1). Birds were randomly and independently selected (with replacement) for the SNP and microsatellite analyses; therefore, there was little overlap among individual birds included in these marker sets. Of the 286 individual birds included in this analysis, 22 were common to both marker sets (15 pre-WNV; 7 post-WNV). The 20-year time period of this study may have encompassed 2–4 breeding cohorts (approximately 1–2 pre- and 1–2 post-WNV, with a sharp turn-over immediately after WNV emergence). Crows can produce offspring as early as two years after hatching, but most do not begin breeding independently until at least 3–4 years after hatching. Breeding initiation is limited at least in part by breeding vacancies, which are created by the death of one or both members of an established breeding pair. Such breeding vacancies likely increased in availability after the emergence of WNV. Microsatellite genotyping. A total of 222 crows (n = 113 and 109 crows pre- and post-WNV, respectively) were genotyped at 34 polymorphic microsatellite loci that were optimized for American crows. Alleles were scored using the microsatellite plugin for Geneious 9.1.8. We used GenePop version 4.7 to test for linkage disequilibrium between all pairs of loci, departures from Hardy–Weinberg equilibrium (HWE), and null allele frequency. Locus characteristics (e.g., alleles/ locus, tests of Hardy–Weinberg equilibrium and null allele frequencies) are given in the supplementary materials (Table S1). Departures from HWE expectations were observed at two loci (PnuA3w from the pre-WNV sample and Cb06 from the post-WNV sample) after Bonferroni correction (Table S1); these loci were removed from subsequent analysis. In 561 pairwise comparisons, four pairs of loci appeared to be in linkage disequilibrium (Cb20 and Cb21; Cb14 and CoBr36; CoBr22 and Cb17, and CoBr12 and Cb10), but this linkage was only apparent at both time points (the pre-WNV and post-WNV populations) for Cb20 and Cb21. We removed both Cb20 and Cb21 from the analysis but retained the other loci because apparent linkage at only a single time point was unlikely to be a result of physical linkage. Two additional loci (Cb17 and Cb10) had a high frequency of null alleles (> 0.1) and were removed from the dataset. All subsequent analyses are therefore based on 28 loci. We scored all birds at a minimum of 26 of these 28 loci, and most (>98%) were scored at all loci (mean proportion of loci typed >0.99). Mean allelic diversity at these loci was 11.25 ± 1.17 alleles/locus (range: 3–31 alleles/locus). Double Digest Restriction Associated DNA (ddRAD) sequencing. We performed ddRAD sequencing on 86 randomly selected crows (43 pre-WNV and 43 post-WNV). 100-500 ng of DNA were digested with SbfI-HF (NEB, R3642L) and MspI-HF (NEB, R016S) restriction enzymes. Samples were ligated with a P2-MspI adapter and pooled in groups of 18-20, each with a unique P1 adapter. Pooled index groups were purified using 1.5X volumes of homemade MagNA made with Sera-Mag Magnetic Speed-beads (FisherSci). Fragments 450-600 bp long were selected using BluePippin (Sage Science) by the Cornell University Biotechnology Resource Center (BRC). After size selection, unique index barcodes were added to each index group by performing 11 cycles of PCR with Phusion® DNA polymerase (NEB). Reactions were purified using 0.7X volumes of MagNA beads and pooled in equimolar ratios for sequencing on the Illumina HiSeq 2500 at the BRC, with single end reads (100 bp). The sequencing was performed with an added Illumina PhiX control (15%) due to low 5’ complexity. Pre- and post-WNV samples were library prepared together and sequenced on a single lane to avoid the introduction of a library or lane effect. We used FASTQC v0.11.9 (Babraham Bioinformatics; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to assess read quality. We trimmed reads to 147 bp using fastX_trimmer (FASTX-Toolkit) to exclude low-quality data at the 3’ end of reads. Next, we eliminated reads with Phred scores below 10, then eliminated reads in which 5% or more bases had Phred scores below 20 (fastq_quality_filter). The fastq files were demultiplexed using the process_radtags module in STACKS v2.52 pipeline to create a file with sequences specific to each individual. We first scaffolded the American Crow reference genome (NCBI assembly: ASM69197v1, Accession no: GCA_000691975.1) into putative pseudochromosomes using the synteny-based Chromosemble tool in Satsuma2 (Grabherr et al. 2010) and the Hooded Crow genome (NCBI assembly: ASM73873v5, Accession no: GCA_000738735.5). We aligned sequence reads to the American Crow pseudochromosome assembly using BWA-MEM (Li & Durbin 2009). We called SNPs in ANGSD (Korneliussen et al. 2014) using the GATK model, requiring SNPs to be present in 80% of the individuals (0.95 postcutoff, SNP p-value 1e-6) with a minimum allele frequency of 0.015. We removed bases with quality scores below 20 (-minQ 20), bad reads (-remove_bads), mapping quality below 20 (-minMapQ20), base alignment quality below 1 (-baq), more than two alleles (-skipTriallelic), and heterozygote bias (-hetbias_pval 1e-5), requiring the minimum depth per individual to be at least two and read depth higher than 1,800. These filters resulted in 16,200 SNPs. To reduce differences in missingness between the pre- and post-WNV populations, we excluded loci that had less than 80% called genotypes per population, resulting in 5,151 SNPs.