In this project, researchers and municipalities examine the possibility of designing personalised reintegration (or 'back to work') strategies for the unemployed. They develop these strategies by pseudonymising and linking various data sources. The overarching question is: which interventions work for whom? And how can data help gain insights?
Stage of the project
The results of the project so far
The completed pilot provides researchers and municipality with clear insights into the unemployed population, clearly showing how current methods work better for certain groups, while others may be approached differently. Furthermore, the data linking aspect led to better understanding of how to organise data collection in municipal context, especially when these data are meant to inform policy. In addition, the citizens' perspective was actively taken into account and used in the conclusions.
What are specific, distinctive, strong elements in this project?
1. Take the time to properly 'clean up' data you want to work with; it often takes a lot of time to be able to work with data sets that were not originally meant for research purposes.
2. Make sure all relevant stakeholders, including the group the research/project concerns, have a seat at the table from day 1.
3. Make sure the data analysis is intertwined with the day-to-day practice the research concerns.
4. Aim to (also) publish results in other ways than only through official publications.
Which specific lessons, do's and don'ts would you like to share? What would be suggestions for others when preparing or implementing the project in their own city?
- Linking (anonymised) data provides insights into the entire (municipal) population, much more than a randomised trial approach would.
- Municipal (policy) insights are paired with interdisciplinary academic expertise.
- Citizens' perspective is actively taken into account.
- The project makes use of municipal statistics as well as national statistics.
- The project not only looks into what is possible and legal when it comes to data-driven approaches, but also looks at what is desirable.