Event
14 Sep 2022

Online African School On Migration Statistics, 12-14 September 2022

  • Date
    12 Sep 2022, 20:09pm
  • Location
    TUNISIA
  • Organizer

    IOM GMDAC

This African Migration Data Network (AMDN) school on migration statistics is the third of its kind after the success of the first two sessions organized in 2020 and 2021. It is a joint initiative between the African Union, IOM and Statistics Sweden. As  migration data are essential to inform policymaking and programming on migration, development and protection  at the national, regional, and international levels, the African Schools on Migration Statistics aim at strengthening national capacities for the collection, production, and dissemination of migration data in African countries. This third edition focuses on diaspora data in particular.

Diasporas provide diverse contributions to sustainable development. They often support countries of origin through financial, social, and cultural transfers, and through demographic, labour, and fiscal contributions.  Diaspora contributions are receiving increasing attention. They have been recognized at a global level, by Objective 19 of the Global Compact for Safe, Orderly and Regular Migration (GCM) and Agenda 2030 in particular, and at the continental level, in documents such as the revised Migration Policy Framework for Africa and AU/ILO/IOM/UNECA Joint Labour Migration Porgramme (JLMP). In addition, countries of origin are increasingly adopting strategies to engage with their diasporas and facilitate their contributions to national development and to other domestic objectives. 

In order to be effective, such policies and programmes need to be informed by evidence. However, availability of diaspora data remains scarce and unequal across regions, due to inherent challenges. For instance, many African countries are not able to provide data for their nationals abroad as well as account for the source of remittances by country of origin and other characteristics. Consequently, member states lack information on the distribution of diaspora members and on their characteristics and needs as well as the concrete contributions they make. In turn, such data gaps hinder the identification of tailored and effective diaspora engagement efforts. Adopted in April 2022, the Dublin Declaration calls to improve data on diasporas in line with SDG target 17.18 and GCM Objective 1. In response to these important needs identified by member states, IOM has recently developed tools in terms of diaspora data such as a Diaspora Mapping Toolkit (2021) and the guide Contributions and Counting: Guidance on Measuring the Economic Impact of your Diaspora beyond Remittances (2020). Disseminating these tools and implementing related training activities is essential to develop member states´ capacities to know their diasporas.

Due to its transnational and country-led nature, the Africa Migration Data Network is in a unique position to contribute to improving data and evidence on diaspora communities and their contributions to development, through this school on migration statistics, which is led by the African Union, IOM and Statistics Sweden.

 

Expected outcomes

Expected outcomes include the following:

  • Increased knowledge on how to collect and process diaspora data, including with the aim to inform diaspora engagement strategies and broader migration and development policies.

  • Increased knowledge of relevant international and continental capacity development material on diasporas.

  • Enhanced understanding of the needs of African states and stakeholders regarding data on diasporas and the added value of collecting data on their human capitals.

  • Define concrete next steps each AMDN focal point and each REC representative can take to help improve collection and procession of diaspora data by each country:

  • Definition of a draft project on diaspora human capital

 

Participation

The African School on Migration Statistics will bring together AMDN focal points, from AU member states and Regional Economic Communities.