Preventing unemployment with Artificial Intelligence

Missing the bigger picture

The trouble with the current process of understanding and counseling clients with job loss is the considerable number of resources required to work through extensive background information of all the customers and get a complete overview of the caseloads. Profiling data per one unemployed person comprises 45 separate indicators, from socio-demographics, prior employment history, labor market characteristics, and up-to-date health data and job vacancies. The abundance of data makes it laborious and error-prone to distinguish the unemployed who have more difficulties getting back into the labor market and should receive more attention and resources. In addition, the state is missing an overview of whether the implemented measures and current labor market policies are relevant and cost-effective.

The risk score analysis is, in principle, an automated version of the Nobel Prize Awarded natural experiments methodology models and principles developed by David Card, Joshua Angrist, and Guido Imbens.

Planning labor policies with real-time data

With the help of Machine Learning, the Estonian government has a management tool for implementing and planning policy measures with a real-time prediction of long-term unemployment risks and automated suggestions of the best ways to prevent them.