Email: matmje@prio.org
Social media, patterns and determinants of migration, climate-conflict nexus, survey methodology, machine learning
PhD student at PRIO.
I have previously worked as research assistant at PRIO on two migration-related projects: QuantMig and FUMI. My work on these projects focused particularly on determinants and drivers of migration and migration aspirations through the use of survey data and systematic literature reviews.
In 2020, I submitted my master's thesis in political science, titled "Rainfall variability and violent, state-based conflict: A machine learning approach to estimate context specificity".
Education:
2018-2020: MA, Political Science, University of Oslo, Sciences Po Paris and PRIO
2014-2017: BA, International Studies, University of Oslo and Sciences Po Paris
Languages spoken:
Norwegian, English, French, Spanish
PRIO Policy Brief
Report - External Series
Report - External Series
Master Thesis
Over the past few decades, thousands of people have responded to survey questions about their thoughts and feelings about possibly migrating. The resulting data can be valuable in migration research but are as good as the questions that are asked in survey. A new paper looks in depth at question formulations and creates an inventory of data.
Thousands of people have responded to surveys with questions about their wishes or plans for migration and researchers have analyzed the data to identify the drivers. But until now, the results have been fragmented. In the first-ever systematic literature review four PRIO researchers map out what makes people want to migrate.
This week Mathilde Bålsrud Mjelva defended her thesis "Rainfall variability and violent, state-based conflict: A machine learning approach to estimate context specificity", and achieved top marks. Congratulations Mathilde!