Periodic Reporting for period 2 - FUME (Future Migration Scenarios for Europe)
Berichtszeitraum: 2020-12-01 bis 2023-05-31
Local circumstances play a key role in migration, from the decision to migrate to settling in destination countries. Most international migrants gravitate towards the largest cities in their destination countries, often after moving internally to a city in their origin country. This trend is also observed in Europe, where population growth in many cities is largely driven by an influx of international migrants.
In countries of origin, major cities serve as gateways to overseas destinations. Many potential migrants first move to larger cities before leaving their country. Therefore, cities in both origin and destination countries significantly influence global migration. To avoid misleading results based solely on the analysis of global or national patterns, understanding migration at local scale is essential.
Consequently, the Future Migration Scenarios for Europe project has focused on specific case areas to analyze migration patterns within and between them. This approach allows for the creation of scenarios that predict how international migration to Europe at different geographic scales may evolve. These scenarios help us better understand and prepare for the various ways migration to the region may unfold in the future.
The project team has worked to identify the major factors that explain international migrantion patterns by examining regional and local circumstances that motivate potential migrants to move, hinder the realization of the migratory project, and attract migrants, as well as to explore how future regional socio-demographic, economic, and environmental challenges could shape migrant movement patterns in Europe.
The main conclusions from this project are:
1. According to all FUME scenarios, the EU will remain a region with positive net migration in the future, with more people immigrating to the EU than emigrating.
2. In the absence of future migration, the population in Europe is projected to decline, and processes of population ageing would occur at an even faster rate.
3. Migration flows are influenced by wars, pandemics, economic and other crises that are difficult to predict, making international migration process highly volatile. At the same time persistent, structural social and economic disparities between world regions lead to constant international migration pressure.
4. Migration triggered by climate change is a rather marginal phenomenon, but may influence migration flows in the future, at regional but potentially also at a more global level.
5. Despite its importance for demographic development in Europe, we face challenges in measuring and analysing migration trends. Challenges include the difficulty of measuring undocumented migration, circular and temporal forms of migration, delayed publication of migration statistics, and varying definitions of migrants and migration that may also change over time and may differ across institutions and countries. To improve our knowledge of migration flows and migrant populations, new forms of data emerging from social media platforms can be used. While these data are instantly available, they face selection bias since not all migrants actively use social media.
6. A combination of newer and more traditional data sources is required to improve our knowledge of migration trends. A collaboration between researchers, statistical offices and policy makers is needed to make more progress in this area to gain a better overview of rapidly changing migrant flows.
7. Nowcasting of migration will become more important. The experience from the Russian attack on Ukraine powerfully shows that migration trends can change rapidly in crisis situations. Methods to ‘nowcast’ migration flows, as they have been developed and tested in the FUME project, can help to provide this type of information almost in real time to observe where and when people move.
In countries of origin, major cities serve as gateways to overseas destinations. Many potential migrants first move to larger cities before leaving their country. Therefore, cities in both origin and destination countries significantly influence global migration. To avoid misleading results based solely on the analysis of global or national patterns, understanding migration at local scale is essential.
Consequently, the Future Migration Scenarios for Europe project has focused on specific case areas to analyze migration patterns within and between them. This approach allows for the creation of scenarios that predict how international migration to Europe at different geographic scales may evolve. These scenarios help us better understand and prepare for the various ways migration to the region may unfold in the future.
The project team has worked to identify the major factors that explain international migrantion patterns by examining regional and local circumstances that motivate potential migrants to move, hinder the realization of the migratory project, and attract migrants, as well as to explore how future regional socio-demographic, economic, and environmental challenges could shape migrant movement patterns in Europe.
The main conclusions from this project are:
1. According to all FUME scenarios, the EU will remain a region with positive net migration in the future, with more people immigrating to the EU than emigrating.
2. In the absence of future migration, the population in Europe is projected to decline, and processes of population ageing would occur at an even faster rate.
3. Migration flows are influenced by wars, pandemics, economic and other crises that are difficult to predict, making international migration process highly volatile. At the same time persistent, structural social and economic disparities between world regions lead to constant international migration pressure.
4. Migration triggered by climate change is a rather marginal phenomenon, but may influence migration flows in the future, at regional but potentially also at a more global level.
5. Despite its importance for demographic development in Europe, we face challenges in measuring and analysing migration trends. Challenges include the difficulty of measuring undocumented migration, circular and temporal forms of migration, delayed publication of migration statistics, and varying definitions of migrants and migration that may also change over time and may differ across institutions and countries. To improve our knowledge of migration flows and migrant populations, new forms of data emerging from social media platforms can be used. While these data are instantly available, they face selection bias since not all migrants actively use social media.
6. A combination of newer and more traditional data sources is required to improve our knowledge of migration trends. A collaboration between researchers, statistical offices and policy makers is needed to make more progress in this area to gain a better overview of rapidly changing migrant flows.
7. Nowcasting of migration will become more important. The experience from the Russian attack on Ukraine powerfully shows that migration trends can change rapidly in crisis situations. Methods to ‘nowcast’ migration flows, as they have been developed and tested in the FUME project, can help to provide this type of information almost in real time to observe where and when people move.
The FUME team has collected and harmonised detailed data on migrant communities and their socio-economic status for the four destination case studies (Copenhagen, Amsterdam, Cracow and Rome). Moreover, global data on migrants per country have been collected and harmonised for use in migration modelling. A Delphi study with migration policy experts has been conducted that served as input during the definition and quantification of the FUME migration scenarios. Interviews with prospective migrants in four countries of origin (Senegal, Iraq, Ukraine and Tunisia) have been conducted with the help of local partners and the migration situation in Central Eastern European countries has been analyzed in-depth. A multi-stage migration model from the global to the regional to the local level has been developed and implemented for the four destination case studies.
Main results achieved in the project:
1. A comprehensive qualitative study of migration aspirations and (cap)abilities in four countries of origin
2. A set of migration scenario narratives and their quantifications
3. A new multidimensional projection model for these scenarios and its outputs
4. A regional and local breakdown of the scenario outputs for the four destination case studies
Results of the project have been exploited and disseminated at several conferences, during workshops with the community, at the final conference in Brussels, May 2023, as well as through two policy briefs. Moreover, the projection data has been published as Open Data.
Main results achieved in the project:
1. A comprehensive qualitative study of migration aspirations and (cap)abilities in four countries of origin
2. A set of migration scenario narratives and their quantifications
3. A new multidimensional projection model for these scenarios and its outputs
4. A regional and local breakdown of the scenario outputs for the four destination case studies
Results of the project have been exploited and disseminated at several conferences, during workshops with the community, at the final conference in Brussels, May 2023, as well as through two policy briefs. Moreover, the projection data has been published as Open Data.
The project has made progress beyond the state of the art in several ways. A new systematic process has been developed for scenario building in migration studies that is based on established methodologies from biological studies. This method focuses on quantifying the key drivers of migration prior to combining them into different scenarios. This way, it connects migration scenarios to future population projections in a less arbitrary, more systematic fashion compared to the approaches previously used for scenario building in this field. Following this approach and combining it with a Delphi survey, a set of six new migration scenarios has been developed and quantified, serving as input to a novel international migration model that breaks down migrant cohorts between 171 countries by age (in 5 year groups), sex, education (in 6 levels) and country of birth. To better understand the local impact of migration, the outputs of the international migration model have been broken down to regional models for selected scenarios in the four destination case study areas. The outputs of the regional model have then been used in a novel spatial disaggregation approach that employs a machine learning model trained on historic settlement data to create high-resolution maps of the projected future distribution of migrant groups in the case study cities.