While the act of profiling jobseekers is as old as employment activation itself, the methods of profiling have changed profoundly. 

Traditionally, employment services have profiled jobseekers in a rule-based manner, often segregating them into large general groups, such as younger versus older, and affording case workers some degree of discretion. More recently, however, governments are increasingly developing and implementing statistical profiling models based on administrative and/or survey data to predict whether a jobseeker will become long-term unemployed.

This development is in line with a broader expectation among governments to conduct evidence-based policy making, to prevent prolonged spells of joblessness, and to tailor services to individuals. One example is the Flemish Employment and Vocational Training Office which has had the opportunity to develop statistical profiling models using modern, data-hungry, and computationally expensive machine learning techniques. These machine learning techniques are better at predicting which jobseekers are at risk of becoming long-term unemployed or of exhausting their benefits than standard regression models.

Given the increasing popularity of statistical profiling and the increasing opportunities to build profiling models, Bert van Landeghem, Sam Desiere and Ludo Struyven have reviewed these practices, discussed how they might inform policy making, and examined the potential moral implications policymakers face when implementing these models to target the unemployed.

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