People at different ages and with different educational background have different migration patterns. However, international migration flows by age and educational attainment are scarce and not always comparable. We use a super learning algorithm with multiple initial models to estimate age and education composition of male and female immigrants for 199 countries over six five-year periods from 1990-1995 to 2015-2020. We access the performance of our approach through cross-validation and comparing initial learners with the super learner algorithm using multiple evaluation metrics. We also compare our age composition estimates against equivalent measures from Eurostat. The estimates indicate that while the proportion of immigration flows with higher education increase in all regions, Europe has the highest immigration flow with secondary or higher education.

Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries1-3. Although long-term migration patterns are often linked to economic development4, they can shift rapidly in response to shocks such as conflict, environmental crises and political change5. Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and are fragmented across incompatible definitions, temporal resolutions and data types6-8. Past efforts have relied on partial datasets, including flow records, stock estimates and model-based reconstructions with limited coverage9-14. A central challenge is therefore to construct a globally consistent, high-resolution account of migration flows over time. Here we present a new dataset of annual origin-destination migration across 230 countries and regions from 1990 to the present, integrating diverse data sources into a unified modelling framework. By combining official statistics, census-based stocks, net migration estimates and past flow reconstructions, our approach produces temporally detailed and spatially comprehensive estimates that substantially extend existing resources. Using an ensemble of deep recurrent neural networks informed by geographic, economic, cultural and political covariates, we capture both persistent trends and short-term responses to changing conditions-all while propagating uncertainty to generate confidence bounds. Our results outperform existing five-year flow estimates on held-out data and provide finer temporal resolution, revealing previously obscured dynamics in global migration patterns. This framework highlights regions in which uncertainty remains high and data collection is most urgently needed. By releasing all data, code and trained models, we provide a transparent and reproducible foundation for future work. These advances enable a more timely and detailed understanding of human mobility, with implications for research and policy in an increasingly dynamic global system.

Existing estimates of human migration are limited in their scope, reliability, and timeliness, prompting the United Nations and the Global Compact on Migration to call for improved data collection. Using privacy protected records from three billion Facebook users, we estimate country-to-country migration flows at monthly granularity for 181 countries, accounting for selection into Facebook usage. Our estimates closely match high-quality measures of migration where available but can be produced nearly worldwide and with less delay than alternative methods. We estimate that 39.1 million people migrated internationally in 2022 (0.63% of the population of the countries in our sample). Migration flows significantly changed during the COVID-19 pandemic, decreasing by 64% before rebounding in 2022 to a pace 24% above the precrisis rate. We also find that migration from Ukraine increased tenfold in the wake of the Russian invasion. To support research and policy interventions, we release these estimates publicly through the Humanitarian Data Exchange.

Although the globalization of international migration is commonly accepted as a general tendency in contemporary migration patterns (de Haas, Castles, and Miller 2020, 9), the corresponding body of empirical evidence is mixed and fragmented. Our review of global migration patterns over the past half-century highlights how the theories, expectations, and ultimately findings may vary depending on the specific definitions, vantage points, and measures being used. In this paper, we provide a simpler and integrated account of the globalization of international migration that includes a corresponding empirical template to quantify the relative importance of two processes at work: the intensity and connectivity of international migration. Using recent estimates of country-to-country migration flows every five years from 1990–1995 to 2015–2020, our analysis using demographic decomposition and group-based multitrajectory modeling highlights the dynamic relationship between intensity and connectivity from both the global and country vantage points. Our work in this paper provides a starting point in the form of a much-needed empirical template, one that is also highly flexible and customizable, for future research on the globalization of international migration to coalesce around and use going forward.

We present a novel and detailed dataset on origin-destination annual migration flows and stocks between 230 countries and regions, spanning the period from 1990 to the present. Our flow estimates are further disaggregated by country of birth, providing a comprehensive picture of migration over the last 43 years. The estimates are obtained by training a deep recurrent neural network to learn flow patterns from 18 covariates for all countries, including geographic, economic, cultural, societal, and political information. The recurrent architecture of the neural network means that the entire past can influence current migration patterns, allowing us to learn long-range temporal correlations. By training an ensemble of neural networks and additionally pushing uncertainty on the covariates through the trained network, we obtain confidence bounds for all our estimates, allowing researchers to pinpoint the geographic regions most in need of additional data collection. We validate our approach on various test sets of unseen data, demonstrating that it significantly outperforms traditional methods estimating five-year flows while delivering a significant increase in temporal resolution. The model is fully open source: all training data, neural network weights, and training code are made public alongside the migration estimates, providing a valuable resource for future studies of human migration.

World Migration ReportVolume 2024, Issue 1 e00036 Original Article Growing migration inequality: What do the global data actually show? Marie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this authorGuy Abel, Guy Abel Professor at the Asian Demographic Research Institute, Shanghai UniversitySearch for more papers by this authorLinda Adhiambo Oucho, Linda Adhiambo Oucho Executive Director, African Migration and Development Policy CentreSearch for more papers by this authorAdam Sawyer, Adam Sawyer Independent ResearcherSearch for more papers by this author Marie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this authorGuy Abel, Guy Abel Professor at the Asian Demographic Research Institute, Shanghai UniversitySearch for more papers by this authorLinda Adhiambo Oucho, Linda Adhiambo Oucho Executive Director, African Migration and Development Policy CentreSearch for more papers by this authorAdam Sawyer, Adam Sawyer Independent ResearcherSearch for more papers by this author First published: 12 June 2024 https://doi.org/10.1002/wom3.36Citations: 1AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Citing Literature Volume2024, Issue1April 2024e00036 RelatedInformation

BACKGROUND International migration is influenced by economic and social factors that change over time. However, given the complexity of these relationships, global population scenarios to date include only stylized migration assumptions that do not account for changes in the drivers of migration. On the other hand, existing projection models of international migration do not resolve all demographic dimensions necessary to interact with the cohort-component models typically used for population projections. OBJECTIVE Here we present a global model of bilateral migration that resolves these dimensions while also accounting for important external, economic, and social factors. METHODS We include age, education, and gender dependencies into a recently developed model of migration by origin, destination, and country of birth. We calibrate the model on bilateral flow data, couple it to a widely used cohort-component population model, and project migration until 2050 under three alternative socioeconomic scenarios. CONCLUSIONS The extended model fits data better than the original migration model and is more sensitive to the choice of socioeconomic scenario, thus yielding a wider range of projections. Regional net migration flows projected by the model are substantially larger than in the stylized assumptions. The largest flows are projected in the most economically unequal scenario, while previously, the same scenario was assumed to have the smallest flows. CONTRIBUTION The results offer an opportunity to reconcile stylized migration assumptions with quantitative estimates of the roles of important migration drivers. The coupled migration- population modeling framework means that interactions between migration and other demographic processes can be captured, and the migration component can be evaluated in more detail than before.

China is one of the major sources of student migrants to many Western countries, growing rapidly during the last couple of decades. In China, several national and regional level policy schemes have been set up to incentivise the return of the high-skilled overseas population. Data to monitor these migration patterns are typically lacking. In this article, we explore the spatial patterns of overseas alumni populations from 106 leading Chinese universities using data gathered from the LinkedIn advertising platform. We first assess the suitability of the LinkedIn data for measuring overseas migrant distributions and then adapt an extended gravity model to aid the interpretation of the relationships between countries, universities and intermediate characteristics and the size of the overseas alumni populations. We find that the LinkedIn data provide plausible measures of Chinese university alumni networks. Alumni populations are in general larger from highly ranked universities, in greater numbers from universities in Beijing and Shanghai and universities with higher numbers of foreign students. These findings help better understand human capital flight, where conventional studies use migration data that do not typically have breakdowns to sub-national units or specify where emigrants received their education.

Although the globalization of international migration is commonly accepted as a general tendency in contemporary migration patterns (de Haas, Castles, and Miller 2020, 9), the corresponding body of empirical evidence is mixed and fragmented. Our review of global migration patterns over the past half-century highlights how the theories, expectations, and ultimately findings may vary depending on the specific definitions, vantage points, and measures being used. In this paper, we provide a simpler and integrated account of the globalization of international migration that includes a corresponding empirical template to quantify the relative importance of two processes at work: the intensity and connectivity of international migration. Using recent estimates of country-to-country migration flows every five years from 1990-1995 to 2015-2020, our analysis using demographic decomposition and group-based multitrajectory modeling highlights the dynamic relationship between intensity and connectivity from both the global and country vantage points. Our work in this paper provides a starting point in the form of a much-needed empirical template, one that is also highly flexible and customizable, for future research on the globalization of international migration to coalesce around and use going forward.

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.

For this special issue of the International Migration Review, we develop and provide a comprehensive organizing framework, the Migration Intersections Grid (MIG), to inform and guide migration research in and through the remainder of the twenty-first century. We motivate our work by conducting a high-level scoping review of summaries and syntheses of different directions of travel in migration research over time. Informed by these results, we then identify and describe 12 components that constitute the MIG, which, as we later discuss, is an interactive intersectional organizing framework. Finally, we illustrate the MIG's interactive intersectional nature by applying it to several areas of migration research where a comprehensive organizing framework of this sort is needed to address existing and emerging issues and questions now and in the coming decades.

Despite being a topical issue in public debate and on the political agenda for many countries, a global-scale, high-resolution quantification of migration and its major drivers for the recent decades remained missing. We created a global dataset of annual net migration between 2000 and 2019 (~10 km grid, covering the areas of 216 countries or sovereign states), based on reported and downscaled subnational birth (2,555 administrative units) and death (2,067 administrative units) rates. We show that, globally, around 50% of the world’s urban population lived in areas where migration accelerated urban population growth, while a third of the global population lived in provinces where rural areas experienced positive net migration. Finally, we show that, globally, socioeconomic factors are more strongly associated with migration patterns than climatic factors. While our method is dependent on census data, incurring notable uncertainties in regions where census data coverage or quality is low, we were able to capture migration patterns not only between but also within countries, as well as by socioeconomic and geophysical zonings. Our results highlight the importance of subnational analysis of migration—a necessity for policy design, international cooperation and shared responsibility for managing internal and international migration.

Universities produce, retain and attract high-skilled individuals and promote economic growth in their cities and surrounding areas. One of the main contributing factors to the impact of universities on local development is the size of alumni that remain after graduation. In recent years, new data sources have emerged from social media that can potentially provide more timely and unique estimates of population redistribution. We use LinkedIn advertising platform data to measure alumni population distributions in South Korea and Taiwan. In both countries, there are decreasing numbers of students entering universities, with potential negative impacts on local development. We validated the LinkedIn data using external comparisons of totals against official data on the distribution of tertiary-educated populations and university student population sizes. We use multi-level gravity models to compare and contrast the spatial distributions of the alumni networks in the two countries, and the related push and pull factors. The data from LinkedIn provide plausible measures of alumni networks and an insight into the potential drivers of alumni population distributions. The results provide useful guidance on the potential impacts when reorganizing university systems in the coming decades.

This research note explores the impact of international migration on global population distribution since the 1990s. The impact of migration on population distribution is a function of both the intensity as well as the effectiveness of migration, that is the imbalance between flows and counter-flows. The presence of reciprocal flows is a well-recognized feature of international migration systems; however, this dimension has been difficult to capture at the global level due to a lack of origin-destination flow data. In this research note, we apply metrics developed for the analysis of internal migration to global migration flows to explore the impact of international migration on global population distribution over time, and across levels of human development. In the five years to 2020, international migration redistributed 0.39 percent of the world's population (28 million people) despite 1.38 percent (101 million) changing country of residence. This has declined from 0.56 percent of the global population in 1990–1995. This decline in impact is underpinned by a reduction in migration effectiveness, that is, the migration system is becoming more balanced over time. The impact of migration was greatest for flows between countries at Very High levels of human development, reflecting high migration intensity and exchanges between countries at Low and Very High levels of human development, reflecting significant asymmetry of flows. Our results suggest systematic shifts in both the level and pattern of flows across the development ladder, with flows becoming more intense and balanced with higher levels of human development.

Understanding and forecasting human mobility in response to climatic and environmental changes has become a subject of substantial political, societal, and academic interest. Quantitative models exploring the relationship between climatic factors and migration patterns have been developed since the early 2000s; however, different models have produced results that are not always consistent with one another or robust enough to provide actionable insights into future dynamics. Here we examine weaknesses of classical methods and identify next-generation approaches with the potential to close existing knowledge gaps. We propose six priorities for the future of climate mobility modeling: (i) the use of non-linear machine-learning rather than linear methods, (ii) the prioritization of explaining the observed data rather than testing statistical significance of predictors, (iii) the consideration of relevant climate impacts rather than temperature- and precipitation-based metrics, (iv) the examination of heterogeneities, including across space and demographic groups rather than aggregated measures, (v) the investigation of temporal migration dynamics rather than essentially spatial patterns, (vi) the use of better calibration data, including disaggregated and within-country flows. Improving both methods and data to accommodate the high complexity and context-specificity of climate mobility will be crucial for establishing the scientific consensus on historical trends and future projections that has eluded the discipline thus far.

Population decline is expected to continue to be a prominent feature of Japanese demography in future decades. Internal migration plays a significant role in dictating the intensity of population decline at the regional level. In this paper we visualize inter-prefecture internal migration flows between the eight regions of Japan in 2020 using a chord diagram to show the relative scales of the origin–destination migration flows between each region. In addition, we use an animated series of chord diagrams to show the development of internal migration between 1954 and 2020. We fix the axis of the sectors of the chord diagram in the animation to illustrate the rapid growth and then slow decline (post-1972) in the regional migration system, as well as the interruption in the movement patterns during 2011, due to the Great East Japan Earthquake.

Females and males often migrate at different rates. Official data on sex-specific international migration flows are missing for most countries, prohibiting comparative measures to identify and address inequalities. Here we use six methods to estimate male and female five-year bilateral migration flows between 200 countries from 1990 to 2020. We validate the estimates from each method through correlations of several migration measures with equivalent reported statistics in countries that collect flow data. We find that the Pseudo-Bayesian demographic accounting method performs consistently better than the other estimation methods for both female and male estimated flows. The estimates from all methods indicate a decline in the share of female migration flows from 1990-1995 to 2005-2010 followed by a recovery over the decade since 2010.

World Migration ReportVolume 2022, Issue 1 e00028 Original Article 7 International Migration as a Stepladder of Opportunity: What do the Global Data Actually Show? Marie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this authorGuy Abel, Guy Abel Professor at the Asian Demographic Research Institute, Shanghai UniversitySearch for more papers by this authorLinda Oucho, Linda Oucho Director of the Research and Data Hub, African Migration and Development Policy CentreSearch for more papers by this authorAdam Sawyer, Adam Sawyer Independent ResearcherSearch for more papers by this author Marie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this authorGuy Abel, Guy Abel Professor at the Asian Demographic Research Institute, Shanghai UniversitySearch for more papers by this authorLinda Oucho, Linda Oucho Director of the Research and Data Hub, African Migration and Development Policy CentreSearch for more papers by this authorAdam Sawyer, Adam Sawyer Independent ResearcherSearch for more papers by this author First published: 18 April 2022 https://doi.org/10.1002/wom3.28AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume2022, Issue1April 2022e00028 RelatedInformation

Evidence-based policies to monitor and manage migration flows require accurate data. Data collection on international migration flow statistics is based on a range of data sources and measures. Discrepancies in reported migration flow data are apparent when comparing flow statistics from receiving countries on the number of arriving migrants by their country of origin with statistics from sending countries on the number of departing migrants by their country of destination. In recent decades the relative incompleteness and non-comparability in reported migration statistics have motivated a number of initiatives to improve data in European countries. In this paper we illustrate graphically the discrepancies between sending and receiving migration flow statistics provided to Eurostat by European countries. We find a reduction of the discrepancies between receiving and sending migration flow data after the implementation of regulations to improve the availability and comparability of migration data.

本文利用人口普查数据,估算了1995-2015年中国地(市)间人口O-D迁移流和迁移率,结合GIS空间分析和社会网络分析方法,揭示了20年间中国人口迁移的时空变化特征.研究发现:①中国人口迁移由相对不活跃、局部地区参与的"低活性时代",逐步走向相对活跃、绝大多数地区参与的"高活性时代".②人口迁移地域类型的时空演化过程呈现出各活跃型地(市)不断扩散,而非活跃型地(市)大幅缩减的特点.③人口迁移网络以"胡焕庸线"为界,东、西两侧迁移流"东密西疏"且差异悬殊,这一空间格局具有很强的稳定性和顽健性.④在人口省内迁移持续增强,以及跨省迁移中沿海三大城市群吸引力的"此消彼长"和西南地区吸引力不断增强的背景下,东中西部地区的人口迁移流场结构表现为:沿海地区主要城市群内部分化和影响范围减弱,中部地区(除湖北省)未能演化出以省为单元的独立社区,西部地区则是西北相对稳定而西南持续变动.