We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

• Constant chronological old-age dependency ratio (Constant OADR scenario)
• Constant prospective old-age dependency ratio (Constant POADR scenario)
• Constant chronological working-age population (Constant WA scenario)
• Constant prospective working-age population (Constant PWA scenario)
• Zero-migration (ZM scenario)

Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

For detailed information about the scenarios and parameters:

Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

All underlying data (UN World Population Prospects 2022) are openly available at:

https://population.un.org/wpp/Download/Archive

Code

1_Data.R:

• Load and merge data from UN World Population Prospects 2022
• Define sample
• Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

2_Scenarios.R:

• Replacement migration scenarios:
  • Constant chronological old-age dependency
  • Constant prospective old-age dependency
  • Constant chronological working-age population
  • Constant prospective working-age population
• Zero-migration scenario

3_Robustness_checks.R:

• Run replacement migrations scenarios with different model sex and age patterns for net migration

Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

Datasets

The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

Acknowledgements

Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

Sustained below replacement fertility leads to declining population size. Several countries in Asia have experienced below replacement fertility for many years. The paper applies a novel approach to examining the viability of using immigration to achieve zero population growth in six Asian countries: China, Japan, Republic of Korea, Thailand, Singapore and Australia. The novel approach is to estimate the level of immigration that would be required to maintain a constant annual number of births in the long term. Maintaining the number of births at the current level is the fastest way to achieve eventual zero population growth. A population with a constant annual number of births, labeled as a quasi-stationary population, also has a near-to constant age structure that is not excessively old. The study concludes that, for all countries except Australia, no reasonable level of immigration could produce a quasi-stationary population if fertility remains at the country's 2020 level. The constraining factors are the current population size and level of fertility and the extent to which there is acceptance of permanent immigrants in the country. If fertility were to increase over 15–20 years to 1.7 births per woman and the country was accepting of relatively large numbers of permanent immigrants, the quasi-stable outcome becomes potentially viable for all countries except China.

The total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

Japan’s ongoing struggle with rapid ageing is well known. Fertility and migration policies have both been proposed as solutions to Japan’s ageing population. We used stock flow population models to estimate the impact of hypothetical fertility and migration policy interventions on measures of aging in Japan from 2015 to 2050. We evaluated policy models based on the Old Age Dependency Ratio (OADR) they produced at the specified end date. Start dates ranged from 2020 to 2030 to assess the time horizons of individual policies. Fertility policies were found to be highly time dependent and only slowed the rate of increase of OADR. It would require a Total Fertility Rate far above replacement levels to compensate for Japan’s already aged demography. Migration policy was less time dependent. However, such measures would require unprecedented, and ultimately unrealistic, volumes of migration over coming decades in order to reduce Japan’s OADR. Our results suggest that fertility and migration based policy responses will be unable to significantly reduce Japan’s OADR or reverse Japan’s ageing population within the next few decades. Japan should focus on activating its human capital through the prolongation of working lives, increasing participation, and improving productivity within the Japanese labour force to mitigate and adapt to the inevitable effects of ageing populations.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

The relative importance of predators and resources (i.e., food) for the dynamics of migratory bird populations is poorly known. Resource availability may be more likely in resource poor environments, but given that nest failure in most systems is due mainly to predation, predator effects may predominate. We document a rapid decline of an isolated Eastern Kingbird (Tyrannus tyrannus) population breeding in the Great Basin Desert of eastern Oregon, USA, and evaluate whether it was driven by limited food resources (water availability ~ food), nest predation, or first-year or adult return rate (RRJ and RRA, respectively) that reflect nonbreeding season events. Most nests failed (~68% of nests) due mainly to nest predation (>90% of failures); nestling starvation was rare. Bioyear precipitation (October-April), breeding season precipitation, and river flow all varied widely but none could account for annual variation in either nest success (NS) or fledging success of successful nest (FSSN)...

Over the recent years, growing number of studies suggests that intensive size-selective fishing can cause evolutionary changes in life-history traits in the harvested population, which can have drastic negative effects on populations, ecosystems and fisheries. However, most studies to date have overlooked the potential role of immigration of fish with different phenotypes as an alternative plausible mechanism behind observed phenotypic trends. Here, we investigated the evolutionary consequences of intensive fishing simultaneously at phenotypic and molecular level in Eurasian perch (Perca fluviatilis L.) population in the Baltic Sea over a 24-year period. We detected marked changes in size- and age-distributions and increase in juvenile growth rate. We also observed reduction of age at sexual maturity in males that has frequently been considered to support the hypothesis of fisheries-induced evolution. However, combined individual-based life-history and genetic analyses indicated increased immigration of foreign individuals with different life-history patterns as an alternative mechanism behind the observed phenotypic change. This study demonstrates the value of combining genetic and phenotypic analyses and suggests that replacement or breakdown of locally adapted gene complexes may play important role in impeding the recovery of fish populations.

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.

From now until 2100, India and China will remain the most populous countries in the world, however China's population decline has already started, and it is on course to fall by around 50 percent in the 2090s; while India's population decline is projected to begin in the 2060s. Of the 10 most populous countries in the world in 2100, five will be located in Asia, four in Africa, as well as the United States. Rapid growth in Africa Rapid population growth across Africa will see the continent's population grow from around 1.5 billion people in 2024 to 3.8 billion in 2100. Additionally, unlike China or India, population growth in many of these countries is not expected to go into decline, and instead is expected to continue well into the 2100s. Previous estimates had projected these countries' populations would be much higher by 2100 (the 2019 report estimated Nigeria's population would exceed 650 million), yet the increased threat of the climate crisis and persistent instability is delaying demographic development and extending population growth. The U.S. as an outlier Compared to the nine other largest populations in 2100, the United States stands out as it is more demographically advanced, politically stable, and economically stronger. However, while most other so-called "advanced countries" are projected to see their population decline drastically in the coming decades, the U.S. population is projected to continue growing into the 2100s. This will largely be driven by high rates of immigration into the U.S., which will drive growth despite fertility rates being around 1.6 births per woman (below the replacement level of 2.1 births per woman), and the slowing rate of life expectancy. Current projections estimate the U.S. will have a net migration rate over 1.2 million people per year for the remainder of the century.

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.

The aims of this project were to:

• understand the demographic changes that United Kingdom local ethnic populations are presently experiencing and are likely to experience in the remainder of the 21st century
• understand the impact that international migration is having on the size and ethnic composition of UK local populations
• understand the role that differences in fertility between the UK's ethnic groups plays in shaping current and future trends
• understand the role that mortality differences between ethnic groups is playing in the changing demography of the UK's local populations
• understand how the ethnic diversity of UK local populations is changing and likely to change in the future
• deliver the projections as a resource for use by social science in the UK
• build capacity in the analysis of demographic change through the development of young and middle career researchers
• tap into the best practice internationally to benefit the UK social science community.

To achieve the project aims, the objectives were to:

• build projections of the populations of ethnic groups for UK local areas
• use the population projection model to explore alternative futures.

The project built a model for projecting the ethnic group populations of UK Local Authorities (LAs), which handles 352 LAs, 16 ethnic groups, 102 ages and 2 sexes. To drive the projections, estimates of the components of ethnic change were prepared for 2001-7. A new method produced UK estimates of ethnic life expectancy, ranging from 82 years for Chinese women to 77 for Pakistani. A future 2% decline in mortality per annum was assumed. Ethnic fertility estimates showed that only Indians, Pakistanis and Bangladeshis had total fertility rates above replacement. Small declines in fertility were forecast. New estimates of the local distribution of immigration were made, using administrative data, because of concerns about official figures. The ethnicity of both immigrants and emigrants for local areas was projected. Estimates were constructed of the ethnic group probabilities for internal in- and out-migration for LAs using 2001 Census data. These probabilities were assumed constant in the future, as migration was stable between 2001 and 2008. Five projections were produced. Two benchmark projections, using constant inputs from 2001-2, forecast the UK population would be 62 and 56 million in 2051.The official projection reports 77 million. The Trend projection, aligned to ONS assumptions projected 78 million for 2051. Using revised assumptions 80 million was projected in a fourth projection. When the model for emigration was changed the projected population was only 71 million. All projections showed ageing and dispersion of ethnic minorities. By 2051 the UK will have a larger, more diverse and integrated population.

For further information about the project, see documentation and the What happens when international migrants settle? Ethnic group population trends and projections for UK local areas under alternative scenarios ESRC award page.

Combined Longitudinal Study of the Second Generation in Spain data set, Waves 1, 2, and 3. This is the publicly available version of the ILSEG data (ILSEG is the Spanish acronym for Investigación Longitudinal de la Segunda Generación, Longitudinal Study of the Second Generation). Questions address the situations and plans for the future of young Spaniards who are children of immigrants to Spain, who were living in Madrid and Barcelona and attending secondary school in 2007-2008 and the 2011-2012 and 2015-2016 follow ups). The longitudinal study of the second Generation (ILSEG in its Spanish initials) represents the first attempt to conduct a large-scale study of the adaptation of children of immigrants to Spanish society over time. To that end, a large and statistically representative sample of children born to foreign parents in Spain or those brought at an early age to the country was identified and interviewed in metropolitan Madrid and Barcelona for wave 1. In total, almost 7,000 children of immigrants attending basic secondary school in close to 200 educational centers in both cities took part in the study. Because of sample attrition, wave 2 introduced a replacement sample. Additionally, a native born sample of children of Spaniards was also included to enable comparisons between native and immigrant-origin populations of the same age cohort.Topics include basic demographics, national origins, Spanish language acquisition, foreign language knowledge and retention, parents' education and employment, respondents' education and aspirations, religion, household arrangements, life experiences, and attitudes about Spanish society. Demographic variables include age, sex, birth country, language proficiency (Spanish and Catalan), language spoken in the home, number of siblings, mother's and father's birth country, religion, national identity, parent's sex, parent's marital status, parent's birth year, and the year the parent arrived in Spain.

Key figures on fertility, live and stillborn children and multiple births among inhabitants of The Netherlands. Available selections: Live born children by sex; Live born children by age of the mother (31 December), in groups; Live born children by birth order from the mother; Live born children by marital status of the mother; Live born children by country of birth of the mother and origin country of the mother; Stillborn children by duration of pregnancy; Births: single and multiple; Average number of children per female; Average number of children per male; Average age of the mother at childbirth by birth order from the mother; Average age of the father at childbirth by birth order from the mother; Net replacement factor. CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin. Data available from: 1950 Most of the data is available as of 1950 with the exception of the live born children by country of birth of the mother and origin country of the mother (from 2021, previous periods will be added at a later time), stillborn children by duration of pregnancy (24+) (from 1991), average number of children per male (from 1996) and the average age of the father at childbirth (from 1996). Status of the figures: The 2023 figures on stillbirths and (multiple) births are provisional, the other figures in the table are final. Changes per 17 December 2024: Figures of 2023 have been added. The provisional figures on the number of live births and stillbirths for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded. When will new figures be published? Final 2023 figures on the number of stillbirths and the number of births are expected to be added to the table in de third quarter of 2025. In the third quarter of 2025 final figures of 2024 will be published in this publication.

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

Changes in Xanthomonas race and species composition causing bacterial spot of tomato have occurred throughout the world and are often associated with epidemics. Knowledge of bacterial population structure is key for resistance discovery and deployment. We surveyed Xanthomonas spp. composition from processing tomato fields in the Midwestern United States over a 4-year period between 2017 and 2020, compared these to strains collected previously, and found that X. perforans is currently the most prevalent species. We characterized 564 X. perforans isolates for sequence variation in avrXv3 to distinguish between race T3 and T4 and validated race designation using hypersensitive response (HR) assays for 106 isolates. Race T4 accounted for over 95% of X. perforans isolates collected in the Midwest between 2017 and 2020. Whole genome sequencing, Average Nucleotide Identity (ANI) analysis, core genome alignment and single nucleotide polymorphism (SNP) detection relative to a reference strain, and phylogenomic analysis suggest that the majority of Midwestern X. perforans strains collected between 2017 and 2020 were nearly identical, with greater than 99.99% ANI to X. perforans isolates collected from Collier County, Florida in 2012. These isolates shared a common SNP variant resulting an a premature stop codon in avrXv3. One sequenced isolate was identified with a deletion of avrXv3 and shared 99.99% ANI with a strain collected in Collier Co., Florida in 2006. A population shift to X. perforans T4 occurred in the absence of widely deployed resistance, with only 7% of tomato varieties tested having the resistant allele at the Xv3/Rx-4 locus. The persistence of nearly identical strains over multiple years suggests that migration led to the establishment of an endemic population. Our findings validate a genomics-based framework to track shifts in X. perforans populations due to migration, mutation, drift, or selection based on comparisons to 146 genomes.

Abstract

This study aims at enlightening the various factors that affect their reintegration in Albania. The study conducted through a national level survey in September-October 2013 suggests that the return migration phenomenon has assumed significant size, particularly after 2009; therefore, the resolving of the problems and the emigrants’ reintegration are the challenges of the Albanian society. Hence, the civil society, the policy makers, the international organizations, the local and national administrative structures, the academic and university community will get hereby a useful tool to understand the problems of migration by contributing to an efficient approach for the reintegration of emigrants into the society. The specific objectives of the survey are:

• To profile return migration to Albania, push and pull factors, characteristics of returning migrants; • To collect information on migrants’ experiences and perceptions of reintegration in Albania; • To formulate several recommendations for further research on return migration as well as the provision of services that facilitate the reintegration of returnees.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey found out that a total of 133, 544 Albanian citizens of the age segment 18- above returned to Albania in the period 2009-2013.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for a particular survey is determined by the accuracy required for the survey estimates for each domain, as well as by the resource and operational constraints. The accuracy of the survey results depends on both the sampling error, which can be measured through variance estimation, and the non-sampling error from all other sources, such as response and other measurement errors, coding and data entry errors. It is important to emphasize that INSTAT recognizes that the sample size of a particular survey is determined by the accuracy required for the national level estimates, as well influenced by logistical issues related to the organization and size of the teams, and the workload for survey administration and data collection. Considering all of these factors, calculations suggested that a sample size of 2000 individuals would give sufficient power to meet the study objectives. When multi-stage sampling is used, the design effect mostly measures the impact of the level of clustering on the sampling efficiency. The design effect depends on the number of sample individuals selected in each stratum. The sample size for

Sampling deviation

The study consisted in a cross-sectional population-based household survey conducted at a national level across each of the 12 prefectures in Albania. A stratified sample designed was used for selecting the individual for sampling. The primary sampling units (PSUs) selected at the first stage are the enumeration areas (EAs), which are small operational areas defined on maps for the 2011 Census enumeration. To control coverage errors, which make the sample less representative, the sampling frame must be of an optimum quality during all the stages of selections. At the first stage, the EA must cover all the areas inhabited by the population under study, without omission or duplication. The boundaries of the EA must be clearly defined and subject to easy identification in the field. SAS software was used at this stage to systematically select the sample of (EAs) with probability proportion to size (PPS) within each prefecture. The second stage of selection dealt with household lists from the selected EAs. The list of households enumerated in the 2011 Census for each sample EA was used as the sampling with equal probability. The third stage of selection was the individual selection in the pre-selected household. The advantages of this two-stage selection procedure are:

1. The selection procedure is simple to implement and reduces possible non-sampling errors in the selection process.
2. It is easy to locate the selected individuals, reducing non-sampling errors and non-response.
3. The interviewers interview only the individuals in the pre-selected households. No replacement of household was permitted, preventing survey bias. When developing the sample selection criteria, special attention was paid to define the study population and its characteristics. Based upon pre-established criteria an individual was considered eligible to be surveyed if he/she was
4. An Albanian citizen, who had migrated abroad and returned to Albania either voluntary or involuntary;
5. 18 years old and above.

The goal was to generate a sample of households that would allow for the production of statistically reliable estimates of the nature and extent of return migration to Albania and reintegration needs of returnees at the national level, and would allow for urban versus rural comparisons.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was structured along three main migratory stages: - Stage 1: Situation before leaving the country of origin; - Stage 2: Experience of migration lived in the main country of immigration; - Stage 3: Return to the country of origin – Postreturn conditions.

The survey was conducted through a structured questionnaire. In line with the objectives of the survey, the contents of the questionnaire were geared towards collecting the amount of necessary information on the following issues:

• Socio-economic conditions and socio-demographic characteristics of returnees prior to departure from Albania, during migration and upon return;
• Reasons for leaving the country and for returning to Albania;
• Experiences of integration in the last country of emigration and their influence on the decision to return to Albania;
• Process of return to Albania and prospect of stay; Links with the last country of immigration;
• Reintegration support and returnee reintegration experiences in Albania.

Response rate

The survey also found that the majority of responses (55%) indicate that employment opportunities should be allocated to enable smooth return and reintegration processes. Financial incentives (25%) were also perceived as important, as well as professional training programs (6%).

Sampling error estimates

The accuracy of the survey results depends on both the sampling error, which can be measured through variance estimation, and the non-sampling error from all other sources, such as response and other measurement errors, coding and data entry errors. It is important to emphasize that INSTAT recognizes that the sample size of a particular survey is determined by the accuracy required for the national level estimates, as well influenced by logistical issues related to the organization and size of the teams, and the workload for survey administration and data collection.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

This file provides county population estimates by age (18 age groups), sex, and race (White, Black, and other races) for July 1st of 1980, 1982, and 1984. The estimates make full use of 1980 census data on gross in- and out- migration for counties and replace the estimates previously labeled "provisional." Data is supplied for each of the 3,136 United States counties and county equivalents as defined in the 1980 census.

Abstract

The national sample survey (NSS), set-up by the government of India in 1950 to collect socio-economic data employing scientific sampling methods, completed its forty-ninth round as a six months survey during the period January to June,1993. Housing condition of the people is one of the very important indicators of the socio-economic development of the country. Statistical data on housing condition in qualitative and quantitative terms are needed periodically for an assessment of housing stock and formulation of housing policies and programmes. NSS 49th round was devoted mainly to the survey on housing condition and migration with special emphasis on slum dwellers. An integrated schedule was designed for collecting data on 'housing condition' as well as ' migration '. Also,households living in the slums were adequately represented in the sample of households where the integrated schedule was canvassed.The present study was different from the earlier study in the sense that the coverage in the present round was much wider. Detailed information on migration have been made with a view to throw data on different facets of migration. For this reason we find separate migration data for males & females, migrant households, return migrants, the structure of the residence of the migrants' households before & after migration, status of the migrants before and after migration and other details on migration. It is to be noted that comprehensive data on out-migrants & return-migrants were collected for the first time in the 49th round.

Geographic coverage

The survey covered the whole of Indian union excepting ( i) Ladakh and kargil districts of Jammu & kashmir ( ii ) 768 interior villages of Nagaland ( out of a total of 1119 villages ) located beyond 5 kms. of a bus route and ( iii ) 172 villages in Andaman & Nicobar islands ( out of a total of 520 villages ) which are inaccessible throughout the year.

Analysis unit

• Households
• Individuals

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage stratified design was adopted for the 49th round survey. The first-stage units(fsu) were census villages in the rural sector and U.F.S. (Urban Frame Survey) blocks in the urban sector (However, for some of the newly declared towns of 1991 census for which UFS frames were not available, census EBs were first-stage units). The second-stage units were households in both the sectors. In the central sample altogether 5072 sample villages and 2928 urban sample blocks at all-India level were selected. Sixteen households were selected per sample village/block in each of which the schedule of enquiry was canvassed. The number of sample households actually surveyed for the enquiry was 119403.

Sample frame for fsus : Mostly the 1981 census lists of villages constituted the sampling frame for rural sector. For Nagaland, the villages located within 5 kms. of a bus route constituted the sampling frame. For Andaman and Nicobar Islands, the list of accessible villages was used as the sampling frame. For the Urban sector, the lists of NSS Urban Frame Survey (UFS) blocks have been considered as the sampling frame in most cases. However, 1991 house listing EBs (Enumeration blocks) were considered as the sampling frame for some of the new towns of 1991 census, for which UFS frames were not available.

Stratification for rural sector : States have been divided into NSS regions by grouping contiguous districts similar in respect of population density and crop pattern. In Gujarat, however, some districts have been split for the purpose of region formation, considering the location of dry areas and distribution of tribal population in the state. In the rural sector, each district with 1981 / 1991 census rural population less than, 1.8 million/2 million formed a separate stratum. Districts with larger population were divided into two or more strata, by grouping contiguous tehsils.

Stratification for urban sector : In the urban sector, strata were formed, within the NSS region, according to census population size classes of towns. Each city with population 10 lakhs or more formed a separate stratum. Further, within each region, the different towns were grouped to form three different strata on the basis of their respective census population as follows : all towns with population less than 50,000 as stratum 1, those with population 50,000 to 1,99,999 as stratum-2 and those with population 2,00,000 to 9,99,999 as stratum-3.

Sample size for fsu's : The central sample comprised of 5072 villages and 2928 blocks. Selection of first stage units : The sample villages have been selected with probability proportional to population with replacement and the sample blocks by simple random sampling without replacement. Selection was done in both the sectors in the form of two independent sub-samples.

Sampling deviation

There was no deviation from the original sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consisted of 13 blocks as given below : Block - 0 : Descriptive Identification of Sample Household Block - 1 : Identification of Sample Household Block - 2 : Particulars of Field Operations Block - 3 : Household Characteristics Block - 4 : Demographic and Migration Particulars of Members of Household Block - 5 : Building and Environment Particulars Block - 6 : Particulars of the Dwelling Block - 7 : Particulars of Living Facilities Block - 8 : Particulars of Building Construction for Residential Purpose Block - 9 : Particulars of Dwelling/Land Owned Elsewhere Block - 10 : Use of Public Distribution System(PDS) Block - 11 : Some General Particulars of Slum Dwellers Block - 12 : Remarks by Investigator Block - 13 : Comments by Supervisory Officer(s)