Opportunities and risks of data-driven labour market policies
How can data-based decisions improve job placement? Using Switzerland as an example, this project highlights the opportunities and challenges posed by data-supported policy models.
Project description (completed research project)
The project investigated how effective labour market policy programmes are in Switzerland – and how their impact can be better targeted. To this end, the researchers developed a recommendation system that helps assign unemployed individuals to more relevant courses or programmes. This should increase their chances of finding a new job. The project used new methods from the field of causal machine learning. These make it possible not only to look at average values, but also to identify exactly which measures are particularly effective for which groups of people.
Background
In recent years, research has made progress in data-driven decision-making in the field of economics. To date, this research has been largely theoretical, so it is important that the new methods are evaluated in practice, specifically in a realistic job placement context, before their potential application.
Aim
The main goal was to evaluate the effectiveness of Swiss labour market policies and to derive an algorithm-based recommendation system that suggests the optimal allocation of specific unemployed people to specific programmes.
Relevance
The project sought to answer practical questions that arise when using data-driven decision-making models for job placement. The project managers therefore worked closely with the State Secretariat for Economic Affairs (SECO) as the potential implementer of the models. Causal machine learning and decision-making algorithms are not limited to the labour market but can also be used in other sectors with similar decision-making processes.
Results
Three main messages
- Use causal machine learning to better understand which public programme works best for whom.
- Use data-based decision algorithms to improve the allocation of individuals to specific programmes.
- Ideally, there should be a continuously updated, integrated data-decision pipeline: a continuous flow of data and a semi-automatic update of the evaluation results from which policy recommendations are derived.
Original title
Chances and risks of data-driven decision making for labour market policy