Description

CRIS is a global research programme, not only because of its expanded and comparative geographical scope, but also and above all because it focuses on a key challenge that stands high in global migration priorities. Migrants’ return and reintegration constitute indeed a pivotal element in current multilateral talks on migration and development.

The objectives of the research programme address three fundamental questions:

                        1. What has been done so far?

The statistical inventory and country reports will critically analyse the characteristics of the mechanisms existing (and non-existing) in the selected third countries. These will be addressed from a political, legal, statistical, and economic point of view.

                        2. What is really happening?

Empirical investigations in selected countries will be carried out. So far, field surveys will be developed in Armenia, Mali and Tunisia. Interviews with returnees will be carried out to understand whether, how, and to what extent pre- and post-return conditions impact on their patterns of reintegration, on their opportunities to stay or re-emigrate, and on their chance to contribute to development back home. Reintegration indicators will be presented.

                        3. What could/should be done?

This question will be addressed following the concrete utilization of datasets and on the occasion of training courses (co-organised with ITC-ILO in Turin, one of the CRIS partner institutions). Among others, a multi-level reflection on returnees’ patterns of reintegration will be fostered, involving scholars from various disciplines, civil servants as well as local actors, associations and NGOs.

 

By analysing the factors shaping return migrants’ patterns of reintegration in their countries of origin, CRIS sets out to collect innovative data and to identify reintegration indicators. The main concern will be to foster a constructive interregional interaction on this issue between stakeholders, through the dissemination of innovative datasets.