Conference Agenda

Anticipating future migration dynamics is central to forming reasonable expectations of economic, demographic, and social developments. However, the discussion around which forecasting methods can provide the most accurate projections of global cross-country migration flows is still contested. As the complexity of migration processes has prevented the evolution of a unified theory of global migration, diverse statistical methods are applied to model migration. The literature typically distinguishes between causal and non-causal/autoregressive models. While the former links push and pull factors of migration to migration patterns, the latter extrapolates human mobility based on past trends. As there is high uncertainty about the adequacy of any individual approach to model migration, we propose a model averaging approach that incorporates the strengths of both causal and autoregressive models. In particular, we consider three non-causal models and a state-of-the-art gravity model to exploit the strengths of diverse modelling techniques. Using OECD data on bilateral migration flows, we conduct a pseudo out-of-sample validation exercise to gain insights into the predictive power of the individual models and their combinations. Our results suggest that model averaging does improve the predictability of migration models and thus provides evidence that model averaging is beneficial in empirical migration research.

During the 21st century, migration became the main component in the increase of the speed of population turnover as well as the main driver of diversity by population origin in Mediterranean metropolitan areas. In a context of lowest-low fertility and rapid population growth, the impact of population turnover and the associated diversity has been vertiginous, both on the territory and on generations, where diversity has become a generational mark for the most recent generations, as well as a differentiating element with respect to the older ones. This impact has been even greater in Mediterranean cities if we consider that most countries in the region were characterized by emigration even at the end of the 20th century. The changes in population turnover have not only affected the territory but have also accelerated the demographic metabolism - understood as the succession of generations - and, with this of social change, both in the growth generations’ size, and in their diversification and the prominence that the immigrant population and diversity have in the younger generations.

In this paper we propose calculating population turnover for the main Mediterranean Spanish metropolis, with very different migratory systems from 2000 to 2024.

Spain is a lowest-low fertility and low-mortality country in which international migration have become the main component of growth in the last two decades, after two unprecedented migration waves peaking in 2007 and 2019, counting 7.5 million foreign born inhabitants (16%) in 2022. For younger generations, e.g. those born in 1986-1995 the share rises to 26%. In the most developed regions the foreign-born share surpasses 20% reaching 40% for the younger generations. The diversification of different generations by origin and educational attainment, accelerates inter and intra generational differences in terms of origin composition, educational attainment and regional polarization. A stronger impact on younger generations has implications for the progression of social change through generational succession, as postulated by the theory of demographic metabolism. Through a multi-state cohort component model, we will project population by place of birth (Spanish and 5 world regions) by three levels of educational attainment for 17 NUTS-2 Spanish regions. We aim to explore human capital and migration through a regional level, with a special focus on future social cohesion and ethno-stratification, especially regarding the more diverse younger generations.

A growing body of literature recognises a 'feminisation of international migration', where women increasingly undertake independent migration. However, it remains unclear how this phenomenon may translate at the aggregate level and over time. This study examines the gender composition of international migration using novel estimates of all country-to-country migration flows across over 195 countries from 1960 to 2020. To produce these estimates, we combine different sources of migration stock data and propose an innovative dynamic estimation strategy, which reconstructs historical geopolitical borders to better identify international migrants. Our preliminary findings point to sharp differences in migrant gender composition around the world. While regions in the Global North appear to host somewhat stable proportions of female immigrants, the gender composition of immigrant populations in the Global South has undergone drastic oscillations over the period under study. Such differences point to gendered processes of destination selection which we aim to explore next.

We developed a hierarchical Bayesian model to quantify international migration in 31 European countries from 2002 to 2022. The approach consistently addresses data quality issues, harmonizes migration definitions, and merges different data sources. We then used the model outcomes to assess the impact of freedom of movement and labor market access in destination countries. Our primary focus is on Poland, a key country among the A-8 countries that joined the EU in 2004. The evidence suggests that the main emigration flows from Poland shifted from Germany to the UK and other countries following Poland's 2004 EU accession. However, in 2011, the pattern reversed when Germany opened its labor market to A-8 country workers, resulting in a significant increase in immigration. As we refine our analyses, we expect to be able to provide more details about the role of migration policy on European migration dynamics.