AI Case Study
IBM Research build machine learning model to analyse migration factors for prediction with low error rates
IBM Research, in conjunction with the Danish Refugee Council, have developed a model to analyse the various factors affecting mixed migration to countries. In trialling the model with migration data from Ethiopia, migration predictions errors were within a few thousands of persons a year. The hope is that the forecasting model provides previously unavailable quantitative insights to policymakers.
Public And Social Sector
A pilot analysis focused on migrants from Ethiopia to six different countries. IBM Research "started by leveraging the 4MI monitoring program run by the DRC through which thousands of migrants on the move are interviewed. Analysis of survey data reveals high-level clusters of drivers for migration. These clusters ranged from lack of rights and other social services, to economic necessity and conflict. These drivers are then mapped to quantitative indicators. Features derived from these indicators are then fed to a model that generates forecasts along with confidence intervals. In addition, the system also generates context for each prediction by showing specific drivers that contributed to the forecast. Using these indicators, we developed an ensemble model to make strategic forecasts annually for bilateral flows on mixed-migration volumes annually."
Budgeting And Forecasting
"In early 2018, with support from IBM Corporate Citizenship and the Danish Ministry for Foreign Affairs, IBM and the Danish Refugee Council (DRC) embarked on a partnership aimed squarely at the need to better understand migration drivers and evidence-based policy guidance for a range of stakeholders.
Understanding migration dynamics and drivers is inherently complex. Circumstances differ from person to person. The question “why did you decide to move?” is not straightforward for people to answer. However, to the extent that individual decisions reflect structural societal factors, the dynamics can be partially explained by aggregate measures."
The analysis resulted in "error rates to be within a few thousand persons per year even for countries with volatile conditions. The system further allows for scenario analysis, where relative changes in influencing factors can be modeled to make adjusted predictions. Such detailed quantitative analysis has previously not been available to stakeholders who need to formulate policy responses."
85 development indicator statistics were used, concerning labour economy, food, education, socio-demographics, infrastructure, strength of institutions, and governance.