Since 2016, IOM’s Migration Governance Indicators (MGI) have helped governments in over 100 countries and 70 cities to take stock of their migration policies and strategies to identify good practices and gaps. Featuring 24 countries and cities that have recently taken advantage of the MGI process, the second edition of the MGI Success Stories highlights how the MGI has been instrumental in strengthening migration frameworks through the development of new policies and laws, and action plans to achieve goals outlined in global commitments, such as the Global Compact for Safe, Orderly and Regular Migration. This booklet offers concise insights that can inspire other governments to use the MGI to harness the power of migration.

This guidance note provides information on why and how to use the MGI results to formulate a baseline for the UNSDCF, which is one way to include migration in this framework. For more general guidance, readers should refer to the forthcoming guidance note on integrating migration into UNSDCFs.

This guidance note provides information on how to use the MGI results in Voluntary National Reviews for Country Offices that have previously participated in the MGI. It should be used to complement IOM’s internal guidance note on Supporting UN Member States in the preparation of Voluntary National Reviews for the 2020 High-level Political Forum, which provides background information on what the HLPF is, what the VNRs are, why they are important, and how IOM can contribute to them.

Created in 2016 by IOM, the Migration Governance Indicators (MGI) programme is a tool that supports governments in taking stock of the comprehensiveness of their migration policies, as well as in identifying gaps and areas that could be strengthened. The MGI framework is aligned with the Global Compact for Safe, Orderly and Regular Migration as the indicators can be mapped out against all the 23 objectives of the Compact. This guidance note aims to illustrate the ways in which governments and other relevant stakeholders can use the MGI in the implementation, follow-up and review of progress towards the achievement of Global Compact objectives.

Créé en 2016 par l'OIM, le programme de Indicateurs de gouvernance des migrations (IGM) est un outil qui aide les gouvernements à faire le point sur l'exhaustivité de leurs politiques migratoires, ainsi qu'à identifier les lacunes et les aspects qui pourraient être renforcés. Le cadre des IGM est aligné sur le Pacte mondial pour des migrations sûres, ordonnées et régulières, car les indicateurs peuvent être alignés par rapport aux 23 objectifs du Pacte. Cette note d'orientation vise à illustrer les manières dont les gouvernements et les autres parties prenantes concernées peuvent utiliser les IGM dans la mise en œuvre, le suivi et l'examen des progrès vers la réalisation des objectifs du Pacte mondial.

This document analyses global, regional and thematic trends that emerge from MGI data in relation to the general commitment and the range of associated actions for each of the 23 Global Compact for Migration objectives. Grounded on the mapping of MGI indicators against Global Compact objectives, data analysis is based on MGI data collected between 2016 and 2021 for 84 countries that voluntarily conducted an MGI assessment and for which data are readily available as of March 2022. The core of the document consists of 23 data bulletins displaying global and regional summary statistics of the answers to the MGI questions mapped against the respective Compact objectives. MGI data are complemented by a series of secondary (external) variables that help contextualize MGI results. This information is complemented by “MGI data insights”, which are text boxes showing glimpses of interesting trends found in the data. Finally, qualitative MGI data are incorporated into the data bulletins by the inclusion of a series of MGI selected examples detailing relevant policies (one for each objective).

This booklet is a compilation of success stories stemming from the Migration Governance Indicators (MGI) process.