Annual number of interprovincial migrants by 5-year age groups and gender for Canada, provinces and territories.

This data collection includes estimates of net migration by age, race, and sex for United States counties for the periods 1950-1960 and 1960-1970. These estimates were developed primarily by the census-survival ratios forward method, and adjusted to be consistent with vital statistics by county. The files contain geographical identifiers such as state, division, region, county name and GEO code. Data on births according to sex and race are presented as well as total population by age groups, sex and race (white vs. nonwhite). Net migration estimates and net migration rates for each category are also included.

Abstract (en): This data collection provides net migration estimates by five-year age groups, Hispanic origin, race, and sex for counties of the United States from 1990 to 2000. These estimates were derived from United States census data from 1990 to 2000, and from vital statistics collected by the National Center for Health Statistics (NCHS) for years 1990 through 1999 using the vital statistics (VS) method. The dataset contains the state and county Federal Information Processing Standards (FIPS) codes that uniquely identify counties within a state. Several data categories are presented in the collection. Vital statistics data tabulate births by sex, race, and Hispanic origin for the periods 1990-1994 and 1995-1999, and deaths by sex, race, Hispanic origin, and age groups for the period 1990-2000. The enumerated and adjusted 1990 and 2000 population categories offer population totals by sex, Hispanic origin, age groups, and race. The expected populations in 2000 are available with totals by sex, race, Hispanic origin, and age groups. Net migration estimates and net migration rates for each category also are included. None Population in the continental United States. Funding insitution(s): United States Department of Agriculture. Forest Service (00-JV-11231300-075). United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD05876). United States Department of Agriculture (WIS04536).

Annual mid-year data on internal migration moves for England and Wales, by local authority, region, single year of age, five-year age group and sex. Data on internal migration moves between local authorities and regions and internal migration moves into and out of each local authority in England and Wales. Also including a lookup table listing each local authority in England and Wales, the region it is located within, its local authority code and region code.

Tokyo Prefecture continued to be a magnet for domestic migration in Japan, attracting nearly 58,500 new residents from other prefectures in 2023. By contrast, Hiroshima Prefecture showed the highest population loss due to outbound migration within the country, with a net loss of approximately 7,400 migrants. Attractiveness of Tokyo The population density in the prefecture has grown over the past two decades, surpassing 6,400 inhabitants per square kilometer in 2020. The appealing nature of Tokyo is also reflected in the age demographics of the metropolis, with most residents falling within the working-age group of 15 to 64 years. Numerous prestigious universities and large corporations make the area a popular destination for young people with aspirations. Depopulation of rural areas The migration patterns across Japan's prefectures indicate significant regional disparities. While Tokyo leads in population size with 14 million inhabitants in 2023, prefectures like Tottori struggle to retain residents, counting only about 540,000 people. This imbalance has prompted concerns about the sustainability of rural communities and has led to various initiatives aimed at revitalizing less populous areas. The Japanese government faces the complex task of addressing these demographic shifts while also navigating challenges resulting from a nationwide aging population due to prolonged life expectancy and fertility decline.

This table contains data for years 2000 - 2011, and is no longer being released. This table contains data described by the following dimensions: Geography; Age group; Migration movement.

Introduction

This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends.

Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years).

Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates.

Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period.

Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health.

Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group.

Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods.

Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

2014-based long term balanced net migration projection for the UK including population by broad age group, components of change and summary statistics. For mid-2014 to mid-2039

Zero net migration (natural change only) variant projection for the UK - population by five-year age groups and sex.

Zero net migration (natural change only) variant projection for Wales including population by broad age group, components of change and summary statistics.

Among people migrating to and from South Korea in 2023, people in the age group between 20 and 29 years comprised the largest group, with around *** thousand migrants. People in their thirties made up the second-largest age group, with about *** thousand migrants.

The Multi-aspect Integrated Migration Indicators (MIMI) dataset is the result of the process of gathering, embedding and combining traditional migration datasets, mostly from sources like Eurostat and UNSD Demographic Statistics Database, and alternative types of data, which consists in multidisciplinary features and measures not typically employed in migration studies, such as the Facebook Social Connectedness Index (SCI). Its purpose is to exploit these novel types of data for: nowcasting migration flows and stocks, studying integration of multiple sources and knowledge, and investigating migration drivers.

The MIMI dataset is designed to have a unique pair of countries for each row. Each record contains country-to-country information about: migrations flows and stock their share, their strength of Facebook connectedness and other features, such as corresponding populations, GDP, coordinates, NET migration, and many others.

Methodology.

After having collected bilateral flows records about international human mobility by citizenship, residence and country of birth (available for both sexes and, in some cases, for different age groups), they have been merged together in order to obtain a unique dataset in which each ordered couple (country-of-origin, country-of-destination) appears once. To avoid duplicate couples, flow records have been selected by following this priority: first migration by citizenship, then migration by residence and lastly by country of birth.

The integration process started by choosing, collecting and meaningfully including many other indicators that could be helpful for the dataset final purpose mentioned above.

• International migration stocks (having a five-year range of measurement) for each couple of countries.
• Geographical features for each country: ISO3166 name and official name, ISO3166-1 alpha-2 and alpha-3 codes, continent code and name of belonging, latitude and longitude of the centroid, list of bordering countries, country area in square kilometres. Also, the following features have been included for each pair of countries: geodesic distance (in kilometres) computed between their respective centroids.

• Non-bidirectional migration measures for each country: total number of immigrants and emigrants for each year, NET migration and NET migration rate in a five-year range.

• Other multidisciplinary indicators (cultural, social, anthropological, demographical, historical features) related to each country: religion (single one or list), yearly GDP at PPP, spoken language (or list of languages), yearly population stocks (and population densities if available), number of Facebook users, percentage of Facebook users, cultural indicators (PDI, IDV, MAS, UAI, LTO). Also the following feature have been included for each pair of countries: Facebook Social Connectedness Index.

Once traditional and non-traditional knowledge is gathered and integrated, we move to the pre-processing phase where we manage the data cleaning, preparation and transformation. Here our dataset was subjected to various computational standard processes and additionally reshaped in the final structure established by our design choices.

The data quality assessment phase was one of the longest and most delicate, since many values were missing and this could have had a negative impact on the quality of the desired resulting knowledge. They have been integrated from additional sources such as The World Bank, World Population Review, Statista, DataHub, Wikipedia and in some cases extracted from Python libraries such as PyPopulation, CountryInfo and PyCountry.

The final dataset has the structure of a huge matrix having countries couples as index (uniquely identified by coupling their ISO 3166-1 alpha-2 codes): it comprises 28725 entries and 485 columns.

Slovenia SI: Net Migration data was reported at 3,319.000 Person in 2024. This records a decrease from the previous number of 5,339.000 Person for 2023. Slovenia SI: Net Migration data is updated yearly, averaging 1,975.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 32,700.000 Person in 1986 and a record low of -8,695.000 Person in 1982. Slovenia SI: Net Migration data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovenia – Table SI.World Bank.WDI: Population and Urbanization Statistics. Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens.;United Nations Population Division. World Population Prospects: 2024 Revision.;Sum;

There were approximately ******* immigrants entering the Republic of Ireland in 2024, compared with ******* in the previous year. During the provided time period, the number of immigrants coming to Ireland peaked at ******* in 2007. Due to the departure of ****** people from Ireland in 2024, the net migration figure for this year was ******.

Projected Population by Age Group, Sex, Race, and Hispanic Origin 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: '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. // 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, it is recommended 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 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.

Components of international migratory increase, quarterly: immigrants, emigrants, returning emigrants, net temporary emigrants, net non-permanent residents.

(StatCan Product) Customization Details: Table A. By province of origin/destination (five-year period) presents information on migration to and from Canadian provinces and territories by Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table B. By age group (five-year period) presents information on in-migrants, out-migrants and net-migrants by the following age group categories: 0-17 years, 18-24 years, 25-44 years, 45-64 years, 65+ years and Total for Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table C. By type of migration and sex (five-year period) presents information on in-migrants, out-migrants and net-migrants by the type of migration (intraprovincial, interprovincial and international) by sex (Male, Female or Both) for Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table D. Flows by CD of origin/destination, or by CMA/non-CMA of origin/destination (five-year period) presents information on where Alberta's migrants/immigrants are moving to and where they've moved from by all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta and internationally from 2004 to 2009. Table E. Median income of migrant taxfilers (single year) is NOT INCLUDED. Annual Migration Estimates - The data consist of estimates of migration flows between census divisions (CDs) or census metropolitan areas (CMAs), by sex and broad age groups. The statistics are derived from the annual tax file provided by the Canada Revenue Agency. Intraprovincial migration: movement of people between two CDs or CMAs located within the same province. The CD/CMA of departure is the CD/CMA of origin and the CD/CMA of arrival is the CD/CMA of destination. Interprovincial migration: movement of people between CDs and CMAs located in two different provinces. The province of departure is the province of origin and the province of arrival is the province of destination. International migration: movement of people between an area in Canada and another country. Migration flows: migration flows for any given CD or CMA. The flows are listed in descending order of net migration for the most recent year of migration. Migration flows: migration flows for any given CD or CMA. The flows are listed in descending order of net migration for the most recent year of migration. There are five standard data tables that are normally available for this product: Table A. By province of origin/destination (five-year period); Table B. By age group (five-year period); Table C. By type of migration and sex (five-year period); Table D. Flows by CD of origin/destination, or by CMA/non-CMA of origin/destination (five-year period); Table E. Median income of migrant taxfilers (single year); Annual Migration Estimates by Census Division/Census Metropolitan Area.

Data Source: CA Department of Finance, Demographic Research Unit

Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj

How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration

Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.

Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.

Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.

Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.

Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.

Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.

Where was the data collected?  Data is collected throughout California. This subset of data includes Napa County.

When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.

Why was the data collected?  The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).

Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf

This dataset contains information from the Office for National Statistics internal migration data for Wales, showing the migrant flows into and out of each local authority in Wales (and Wales itself) to and from other parts of the UK, and also a net position, by sex and quinary (five-year) age group. Note that data for Wales as a whole will not be the sum of individual local authority data as moves between local authorities within Wales will not contribute to the flows into or out of Wales.